Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Multiple constraints cause positive and negative feedbacks limiting grassland soil CO2efflux under CO2enrichment

Terrestrial ecosystems are increasingly enriched with resources such as atmospheric CO2that limit ecosystem processes. The consequences for ecosystem carbon cycling depend on the feedbacks from other limiting resources and plant community change, which remain poorly understood for soil CO2efflux, JCO2, a primary carbon flux from the biosphere to the atmosphere. We applied a unique CO2enrichment gradient (250 to 500 μL L-1) for eight years to grassland plant communities on soils from different landscape positions. We identified the trajectory of JCO2responses and feedbacks from other resources, plant diversity [effective species richness, exp(H)], and community change (plant species turnover). We found linear increases in JCO2on an alluvial sandy loam and a lowland clay soil, and an asymptotic increase on an upland silty clay soil. Structural equation modeling identified CO2as the dominant limitation on JCO2on the clay soil. In contrast with theory predicting limitation from a single limiting factor, the linear JCO2response on the sandy loam was reinforced by positive feedbacks from aboveground net primary productivity and exp(H), while the asymptotic JCO2response on the silty clay arose from a net negative feedback among exp(H), species turnover, and soil water potential. These findings support a multiple resource limitation view of the effects of global change drivers on grassland ecosystem carbon cycling and highlight a crucial role for positive or negative feedbacks between limiting resources and plant community structure. Incorporating these feedbacks will improve models of terrestrial carbon sequestration and ecosystem services

Multiple constraints cause positive and negative feedbacks limiting grassland soil CO2efflux under CO2enrichment

Terrestrial ecosystems are increasingly enriched with resources such as atmospheric CO2that limit ecosystem processes. The consequences for ecosystem carbon cycling depend on the feedbacks from other limiting resources and plant community change, which remain poorly understood for soil CO2efflux, JCO2, a primary carbon flux from the biosphere to the atmosphere. We applied a unique CO2enrichment gradient (250 to 500 μL L-1) for eight years to grassland plant communities on soils from different landscape positions. We identified the trajectory of JCO2responses and feedbacks from other resources, plant diversity [effective species richness, exp(H)], and community change (plant species turnover). We found linear increases in JCO2on an alluvial sandy loam and a lowland clay soil, and an asymptotic increase on an upland silty clay soil. Structural equation modeling identified CO2as the dominant limitation on JCO2on the clay soil. In contrast with theory predicting limitation from a single limiting factor, the linear JCO2response on the sandy loam was reinforced by positive feedbacks from aboveground net primary productivity and exp(H), while the asymptotic JCO2response on the silty clay arose from a net negative feedback among exp(H), species turnover, and soil water potential. These findings support a multiple resource limitation view of the effects of global change drivers on grassland ecosystem carbon cycling and highlight a crucial role for positive or negative feedbacks between limiting resources and plant community structure. Incorporating these feedbacks will improve models of terrestrial carbon sequestration and ecosystem services

CO2 enrichment and soil type additively regulate grassland productivity

The development of a predictive understanding of how atmospheric CO2 enrichment is affecting the primary productivity of the terrestrial biosphere is among the most pressing of ecological challenges. The terrestrial biosphere absorbs c. 25% of anthropogenic carbon (C) emissions (Le Quere et al., 2018). Uncertainty in CO2 effects on ecosystem C uptake is a major constraint in the prediction of C cycling and the provisioning of productivity- related ecosystem services. Grasslands cover c. 25% of the terrestrial area and are an important contributor to the global C balance (Sala et al., 1996). CO2 enrichment stimulates the aboveground net primary productivity (ANPP) of most water-limited grasslands by increasing plant water use efficiency (WUE; productivity per unit of transpiration; Morgan et al., 2004; Nowak et al., 2004; Fatichi et al., 2016), but grassland ANPP, as other ecosystem functions, is determined by drivers in addition to water availability which act simultaneously and often interactively with CO2 (Polley et al., 2011). CO2 enrichment usually shows greater stimulation of plant productivity when nitrogen (N) availability is relatively high (Owensby et al., 1994; Reich & Hobbie, 2013; Mueller et al., 2016), for example. Other drivers include precipitation timing (Hovenden et al., 2014), disturbance regimes (Newton et al., 2014), plant species composition (Langley & Megonigal, 2010; Fay et al., 2012; Polley et al., 2012) and soil properties (Epstein et al., 1997, 1998), including soil texture, which influences water availability to plants (Tor-Ngern et al., 2017)

CO2 enrichment and soil type additively regulate grassland productivity

The development of a predictive understanding of how atmospheric CO2 enrichment is affecting the primary productivity of the terrestrial biosphere is among the most pressing of ecological challenges. The terrestrial biosphere absorbs c. 25% of anthropogenic carbon (C) emissions (Le Quere et al., 2018). Uncertainty in CO2 effects on ecosystem C uptake is a major constraint in the prediction of C cycling and the provisioning of productivity- related ecosystem services. Grasslands cover c. 25% of the terrestrial area and are an important contributor to the global C balance (Sala et al., 1996). CO2 enrichment stimulates the aboveground net primary productivity (ANPP) of most water-limited grasslands by increasing plant water use efficiency (WUE; productivity per unit of transpiration; Morgan et al., 2004; Nowak et al., 2004; Fatichi et al., 2016), but grassland ANPP, as other ecosystem functions, is determined by drivers in addition to water availability which act simultaneously and often interactively with CO2 (Polley et al., 2011). CO2 enrichment usually shows greater stimulation of plant productivity when nitrogen (N) availability is relatively high (Owensby et al., 1994; Reich & Hobbie, 2013; Mueller et al., 2016), for example. Other drivers include precipitation timing (Hovenden et al., 2014), disturbance regimes (Newton et al., 2014), plant species composition (Langley & Megonigal, 2010; Fay et al., 2012; Polley et al., 2012) and soil properties (Epstein et al., 1997, 1998), including soil texture, which influences water availability to plants (Tor-Ngern et al., 2017)

Search for a vector-like quark T′ → tH via the diphoton decay mode of the Higgs boson in proton-proton collisions at s \sqrt{s} = 13 TeV

A search for the electroweak production of a vector-like quark T′, decaying to a top quark and a Higgs boson is presented. The search is based on a sample of proton-proton collision events recorded at the LHC at = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. This is the first T′ search that exploits the Higgs boson decay to a pair of photons. For narrow isospin singlet T′ states with masses up to 1.1 TeV, the excellent diphoton invariant mass resolution of 1–2% results in an increased sensitivity compared to previous searches based on the same production mechanism. The electroweak production of a T′ quark with mass up to 960 GeV is excluded at 95% confidence level, assuming a coupling strength κT = 0.25 and a relative decay width Γ/MT′ < 5%

Measurement of the Higgs boson inclusive and differential fiducial production cross sections in the diphoton decay channel with pp collisions at s \sqrt{s} = 13 TeV

The measurements of the inclusive and differential fiducial cross sections of the Higgs boson decaying to a pair of photons are presented. The analysis is performed using proton-proton collisions data recorded with the CMS detector at the LHC at a centre-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 137 fb$^{−1}$. The inclusive fiducial cross section is measured to be $σ_{fid}$=73.4$^{+5.4}_{−5.3}$(stat)$^{+2.4}_{−2.2}$(syst) fb, in agreement with the standard model expectation of 75.4 ± 4.1 fb. The measurements are also performed in fiducial regions targeting different production modes and as function of several observables describing the diphoton system, the number of additional jets present in the event, and other kinematic observables. Two double differential measurements are performed. No significant deviations from the standard model expectations are observed

Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at s \sqrt{s} = 13 TeV

A search for the pair production of the lightest supersymmetric partner of the top quark, the top squark (t∼1), is presented. The search targets the four-body decay of the t∼1, which is preferred when the mass difference between the top squark and the lightest supersymmetric particle is smaller than the mass of the W boson. This decay mode consists of a bottom quark, two other fermions, and the lightest neutralino (χ∼01), which is assumed to be the lightest supersymmetric particle. The data correspond to an integrated luminosity of 138 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment at the CERN LHC. Events are selected using the presence of a high-momentum jet, an electron or muon with low transverse momentum, and a significant missing transverse momentum. The signal is selected based on a multivariate approach that is optimized for the difference between m(t∼1) and m(χ∼01). The contribution from leading background processes is estimated from data. No significant excess is observed above the expectation from standard model processes. The results of this search exclude top squarks at 95% confidence level for masses up to 480 and 700 GeV for m(t∼1) − m(χ∼01) = 10 and 80 GeV, respectively