Supporting use of evidence in argumentation through practice in argumentation and reflection in the context of SOCRATES learning environment

The aim of this study was to examine how students used evidence in argumentation while they engaged in argumentive and reflective activities in the context of a designed learning environment. A web-based learning environment, SOCRATES, was developed, which included a rich data base on the topic of Climate Change. Sixteen 11th graders, working with a partner, engaged in electronic argumentive dialogs with classmates who held an opposing view on the topic and in some evidence-focused reflective activities, based on transcriptions of their dialogs. Another sixteen 11th graders, who studied the data base in the learning environment for the same amount of time as experimental-condition students but did not engage in an argumentive discourse activity, served as a comparison condition. Students who engaged in an evidence-focused dialogic intervention increased the use of evidence in their dialogs, used more evidence that functioned to weaken opponents’ claims and used more accurate evidence. Significant gains in evidence use and in meta-level communication about evidence were observed after students engaged in reflective activities. We frame our discussion of these findings in terms of their implications for promoting use of evidence in argumentation, and in relation to the development of epistemological understanding in science

En ny tilnærming for å forutsi melkeproduksjonsrespons og fôrutnyttelse hos melkekyr

Feed evaluation systems (FESs), such as the Nordic Feed Evaluation System (NorFor), aim to help dairy producers compare the nutritive value of various types of feed, formulate of balanced diets, and predict animal performance given a certain diet. To cover the changing demands of the dairy industry, FESs are in continuous need of improvement and updating. Therefore, this thesis contributes to improving NorFor by developing a model that can estimate the energy content of compound feeds and the development of a model describing milk production responses to the changes in silage digestibility and concentrate intake. The practical use of this knowledge is central because the common goal is to implement them into NorFor for use in farms. For the development of the energy estimation model of compound feeds, the activities are described in two scientific articles (Papers I and II). Paper I show that the enzymatic digestibility of the organic matter method (EDOM) is precise for the determination of organic matter digestibility (OMD) of compound feeds, here with a high correlation between measures of a sample within and between labs. This is vital for use in practice because a high correlation between the measurements of a sample means that values can be compared between countries and through time. Thus, is a viable method if OMD is to be included in the energy estimation equation developed in Paper II. The best fit model (2.08% RMSE) for estimating the net energy of lactation at 20 kg DM (NEL20) of compound feeds include OMD measured by EDOM and the chemical components crude fat, NDF, and crude protein (urea corrected) as the explanatory variables. The model can be used as an alternative when information about ingredients for the calculation of NEL20 in the mechanistic model is lacking. For the development of the responses in milk production regarding changes in diet, a meta-analysis was developed in Paper III. The meta-analysis showed a curvilinear response of ECM to concentrate intake. Hence, ECM showed a decreasing increment to concentrate intake. This marginal response was also affected by silage digestibility, showing lower responses with increasing silage digestibility. Paper IV is the result of an animal experiment involving 60 Norwegian Red cows. The objective was to test the meta-analysis by feeding two silages with different digestibility and concentrate levels. Milk production, intake, and methane emissions were measured throughout the experiment. The x silage with a higher digestibility was able to maintain high milk yields with low concentrate supply levels, but this could not be achieved with silage with lower digestibility. When the concentrate supply was increased, low-digestible silages showed a higher response in milk production. Cows in the high-digestible silage treatment were offered a lower concentrate supply, but the methane production did not differ between the silage treatments. Methane per unit of feed and milk decreased with increasing total intake, regardless of the type of silage or level of concentrate. Paper V produced, based on the meta-analysis, a developed response approach that could be adapted to farm data, here by transforming the responses variable—ECM—into concentrate efficiency responses. According to an algorithm developed using machine learning, silage is classified as high or low digestibility. A silage is classified as highly-digestible because of the steeper decrease in concentrate efficiency with the increase in concentrate supply because of lower marginal milk responses. The algorithm also allows the evaluation of the concentrate efficiency for a specific silage use in farms at both, group and individual cow level. An economic response approach was also developed, showing that for highly-digestible silages, the optimal concentrate supply level is sensitive to changes in concentrate prices.TINE SA NorFor Norges forskningsrådacceptedVersion2021:9

Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

peer-reviewedIn vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro

Measuring Welfare Loss Caused by Air Pollution in Europe: A CGE Analysis

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).To evaluate the socio-economic impacts of air pollution, we develop an integrated approach based on computable general equilibrium (CGE). Applying our approach to Europe shows that even there, where air quality is relatively high compared with other parts of the world, health-related damages caused by air pollution are substantial. We estimate that in 2005, air pollution in Europe caused a consumption loss of around 220 billion Euro (year 2000 prices, around 3 percent of consumption level) and a social welfare loss of around 370 billion Euro, measured as the sum of lost consumption and leisure (around 2 percent of welfare level). In addition, we estimated that a set of 2020-targeting air quality improvement policy scenarios, which are proposed in the 2005 CAFE program, would bring 18 European countries as a whole a welfare gain of 37 to 49 billion Euro (year 2000 prices) in year 2020 alone.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Modelling the emergent dynamics and major metabolites of the human colonic microbiota

Funded by Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS) Acknowledgements We would like to thank Thanasis Vogogias, David Nutter and Alec Mann for their assistance in developing the software for this model. We also acknowledge the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) for their financial support. Furthermore,many thanks go to the two anonymous reviewers whose hard work has greatly improved this paper.Peer reviewedPublisher PD

MULTI-GAS EMISSION REDUCTION FOR CLIMATE CHANGE POLICY: AN APPLICATION OF FUND

The costs of greenhouse gas emission reduction are investigated with abatement of carbon dioxide, methane, and nitrous oxide using the FUND model. The central policy scenario keeps anthropogenic radiative forcing below 4.5 Wm-2. If CO2 emission reduction were the only possibility to meet this target, the net present value of consumption losses would be $45 trillion; with abatement of the other gases added, costs fall to$33 trillion. The bulk of these costs savings can be ascribed to nitrous oxide. Because nitrous oxide is so much more important than methane, the choice of equivalence metric between the greenhouse gases does not matter much. Sensitivity analyses show that the shape of the cost curves for CH4 and N2O emission reduction matters, and that the inclusion of SO2 and sulphate aerosols make policy targets substantially harder to achieve. The costs of emission reduction vary greatly with the choice of stabilisation target. A target of 4.5 Wm-2 is not justified by our current knowledge of the damage costs of climate change.Climate change, emission reduction, carbon dioxide, methane, nitrous oxide

Analysis of Climate Policy Targets under Uncertainty

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Although policymaking in response to the climate change is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions targets developed originally for a study by the U.S. Climate Change Science Program. Results are shown for atmospheric concentrations, radiative forcing, sea ice cover and temperature change, along with estimates of the odds of achieving particular target levels, and for the global costs of the associated mitigation policy. Comparison with other studies of climate targets are presented as evidence of the value, in understanding the climate challenge, of more complete analysis of uncertainties in human emissions and climate system response.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.Comment: 36 pages, 4 figures, 2 table