12 research outputs found

    Ecological goods and services and agroforestry : the benefits for farmers and the interests for society

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    Paper presented at the 11th North American Agroforesty Conference, which was held May 31-June 3, 2009 in Columbia, Missouri.In Gold, M.A. and M.M. Hall, eds. Agroforestry Comes of Age: Putting Science into Practice. Proceedings, 11th North American Agroforestry Conference, Columbia, Mo., May 31-June 3, 2009.The main objective of this project is to estimate the social value of environmental goods and services (EG&S) generated by agroforestry practices and to evaluate the profitability of these practices for agricultural producers and for society. Two agroforestry practices are considered: riparian buffer zones and windbreaks. Moreover, the situations in two representative agricultural watersheds serve our analysis (Chateauguay and Fouquette watersheds). Among the numerous EG&S that are provided through agroforestry practices, nine have been chosen for this study: agriculture-related odors, aestheticism of the landscape, terrestrial biodiversity, surface water quality, carbon sequestration, road accidents, clearing snow from roads, treatment of drinking water and wild pollinating insects. Several economic valuation methods have been used, such as hedonic pricing value, benefit transfer, productivity method or experimental economics.Jean Nolet (1), Claude Sauve_ (1), Maria Olar (1), Maribel Hernandez (1), Marjolaine Mondon (1), Caroline Simard (1), Louis-Samuel Jacques (1), Andr� V�zina (2), Nathan De Baets (2), Maud Ablain (3), and Pierre Etcheverry (3) ; 1. EcoRessources Consultants, 825, rue Raoul-Jobin, Qu�bec, QC, Canada, G1N 1S6. 2. Activa Environnement Inc., 106, rue Industrielle, New Richmond (Qu�bec), G0C 2B0. 3. Centre d'expertise sur les produits agroforestiers (CEPAF), 235, route 230 ouest, La Pocati�re, QC, Canada, G0R 1Z0.try pIncludes bibliographical references

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Ecological Discomforts and How to Study Them

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    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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