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Record W2966275731

Supporting Nutrition Sensitive Agriculture through neglected and underutilized species: Operational framework

2019· report· en· W2966275731 on OpenAlexfundno aff
S. Padulosi, Roy Phrang, Francisco J. Rosado-May

Bibliographic record

VenueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2019
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicSeed and Plant Biochemistry
Canadian institutionsnot available
FundersConsortium of International Agricultural Research CentersGovernment of CanadaInternational Fund for Agricultural DevelopmentChristensen Fund
KeywordsAgricultureBusinessEnvironmental planningAgroforestryNatural resource economicsEnvironmental resource managementEnvironmental scienceBiologyEconomicsEcology
DOInot available

Abstract

fetched live from OpenAlex

This joint Bioversity-IFAD publication was developed to guide IFAD and other agencies’ efforts in leveraging neglected and underutilized species (NUS) in support of Nutrition Sensitive Agriculture. It presents a holistic value chain approach for the use enhancement of these local resources in project design and implementation in order to attain more resilient production and food systems. It also highlights ways by which their better use can contribute towards the social and economic empowerment of marginalized groups, including women an Indigenous Peoples, who play an essential role in safeguarding their genetic diversity and associated traditional knowledge.

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)0.038
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0050.008
Scholarly communication0.0170.009
Open science0.0040.021
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.086
GPT teacher head0.366
Teacher spread0.281 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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