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Record W2273202538 · doi:10.13140/rg.2.2.34164.40329

Report of the work of the expert group on maintaining the ability of biodiversity to continue to support the water cycle

2012· article· en· W2273202538 on OpenAlexaff
Mike Acreman, Juliana Albertengo, Telmo Jorge Carneiro Amado, Mao Amis, Aileen Anderson, Isam Bacchur, Gottlieb Basch, Ademir Calegari, Nick A. Chappell, Nakul Chettri, David Coates, Emmanuelle Cohen-Shacham, Sandra Corsi, Nick C. Davidson, Carlos Rogério de Mello, Renate Fleiner, Theodor Friedrich, G. Lukacs, T. Goddard, Emilio J. González-Sánchez, Hans M. Gregersen, Richard R. Harwood, M. M. Mammed Oda Hussein, Amir Kassam, Ike-Jae Kim, Kwi‐Gon Kim, François Laurent, Hongwen Li, Matthew McCartney, Rob Mc Innes, Ivo Mello, Patricia Moreno‐Casasola, Aziz Nurbekov, Tomasz Okruszko, R. Peiretti, Jules Pretty, Ricardo Ralisch, Jõa Carlos Morales Sá, Moshiri Shahriar, Asif Sharif, A. B. Shrestha, Waidi Sinun, Wolfgang G. Sturny, Christian Thierfelder, Norman Uphoff, S P Wani, Ekatarina Yakushina

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of OttawaMcGill UniversityAgriculture Food and Rural Development
Fundersnot available
KeywordsBiodiversityWork (physics)Plan (archaeology)Section (typography)Environmental resource managementBusinessPolitical scienceSustainabilityAgricultureSubject (documents)Environmental planningPublic relationsGeographyEngineeringComputer scienceEcologyLibrary scienceEconomicsBiology

Abstract

fetched live from OpenAlex

rapport d'expertise pour l'UNEP (Programme des Nations Unies sur l'Environnement)

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)0.013
metaresearch head score (Gemma)0.010
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: Review · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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
GenreReview

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

Citations4
Published2012
Admission routes1
Has abstractyes

Explore more

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