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Record W4414833673

Seeds that give revisited: Participatory plant breeding and rural revitalization

2023· book· en· W4414833673 on OpenAlexafffundabout
Yiching Song, Ronnie Vernooy, Yongping Yang, S. Ceccarelli, Alessandra Galié, Stefania Grando, Eva Weltzien, Fred Rattunde, M. Sidibe, Kirsten Vom Brocke, Abdoulaye Diallo, Bettina I. G. Haussmann, Bocar Diallo, Baloua Nebié, Aboubacar Touré, Anja Christinck, Hilton Mbozi, J. N. Mushonga, Patrick Kasasa, Marvin Gómez, Juan Carlos Rosas, Sally Humphries, José Jiménez, Paola Orellana, Carlos Federico Domínguez Ávila, Mérida Barahona, Fredy Sierra, Pitambar Shrestha, Normita G. Ignacio, Norminda P. Naluz, Kaijian Huang, Milin Tian, Xin Song, Guanqi Li, Yufen Chuang, Yanyan Zhang, Yunyue Wang, Lu ChunMing, Guangyu Han, Yifan Zhu, Bo Jiang, Ling Huang, Hongsong Wang, Xiang Li, Chuanhua Chen

Bibliographic record

VenueAgritrop (Cirad) · 2023
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversity of Guelph
FundersGuangxi Academy of Agricultural SciencesInternational Development Research Centre
KeywordsCitizen journalismWork (physics)Participatory action researchInternational developmentParticipatory evaluation
DOInot available

Abstract

fetched live from OpenAlex

In 2003, the International Development Research Centre (IDRC) of Canada published “Seed that give. Participatory plant breeding,” synthesizing the achievements and lessons learned of the first 10 (pioneering) years of participatory plant breeding (PPB), a concept first tabled at an international workshop in Wageningen, the Netherlands, in 1994. IDRC was one of the early and most fervent supporters of PPB. See: https://publications.gc.ca/site/eng/9.648982/publication.html In “Seeds that give revisited,” the PPB champions highlighted in the 2003 book present and reflect on their work over the last 20 years, joined by a group of Chinese professionals who were inspired by the early PPB work, bringing the approach to new regions and crops. Contributors write about significant results obtained, but also about (new) challenges. The Conclusion brings the cases together.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.515
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.220
Teacher spread0.178 · 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 teacher head, not a consensus.

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

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
Published2023
Admission routes3
Has abstractyes

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