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Synopsis numéro spécial « légumineuses »

2017· preprint· en· W2784169966 on OpenAlexaboutno aff
Gérard Duc, Jean‐Michel Chardigny

Bibliographic record

VenueProdinra (INRA Bordeaux-Aquitaine) · 2017
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceMolecular biologyArtBiology

Abstract

fetched live from OpenAlex

Synopsis special issue “Legumes” Agricultural plant production in occidental countries evolved during the 20th century towards more specialized and intensive systems, mobilizing large inputs of agrochemicals and decreasing legumes’ production. This situation leads to agronomic difficulties resulting in low diversity within cropping systems, in reduced protein autonomy of several countries, in pollutions and in increased plant stress frequencies in relation to climate change. On the other hand, the human diet has changed with an increasing meat protein intake in high GDP countries, thus lowering the incorporation of pulse seed in foods; and simultaneously health problems resulting from malnutrition have increased in areas with lower GDP. These facts highlight the huge need for rapid reactions at world and territorial scales, rethinking and reconnecting agricultural and food systems towards healthy and sustainable situations. Possible levers include larger area for legume cropping. Even if these productions are nowadays very low in France and Europe (<3% of arable land) they offer potential benefits since these plants (i) establish a symbiosis with soil rhizobia bacteria that allows them to fix nitrogen from the atmosphere and (ii) the products of these crops are sources of protein and have been recognized with nutritional and health values. They also represent key possibilities for developing the protein autonomy in France and the EU. 2016 was declared by FAO as the International Year of Pulses. Against this backdrop of increasing global demand for sustainable protein for food and feed and for climate change mitigation, this opportunity led INRA, CIRAD, Terres Univia and Terres Inovia to organize the 1st "Francophone meeting on legumes" (RFL1) (May 31 - June 1st 2016 in Dijon, France) with attendees from the whole sector, i.e. scientists as well as technical and professional organizations, to discuss the multiple issues and opportunities of seed and forage legumes. Besides French actors, representatives of countries such as Canada or those of the African continent, which are very concerned in various ways for these productions, were targeted; and therefore a French-speaking international perimeter was chosen by a scientific and technical committee for this meeting, Various sessions provided an opportunity to review the current bottlenecks and levers for the development of legumes for human and animal consumption, ranging from the organizational level of policies and sectors, through the levers in culture that would allow an increase in production volumes (new cropping systems, varieties, efficiency of symbiosis), to the quality aspects of raw products or processes that can improve nutritional value or health and bring added value (new production systems and varieties, new products in food or in animal husbandry systems). Impacts on sustainability of production or food have been considered within all of these strategies. The contributions of this special issue are representative of the papers presented during the meeting, which involved more than 250 attendees.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.146
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1460.105

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.285
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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