QUANTITATIVE ANALYSIS OF DATA FROM PARTICIPATORY METHODS IN PLANT BREEDING
Bibliographic record
Abstract
Although participatory plant breeding (PPB) is gaining greater acceptance worldwide, the techniques needed to analyze the data from participatory methodologies in the context of plant breeding are still not well known or understood. Scientists from different disciplines and cropping backgrounds, working in international research centers and universities, discussed and exchanged methods and ideas at a workshop on the quantitative analysis of data from participatory methods in plant breeding. The papers in this volume address the three themes of the workshop: designing and analyzing joint experiments involving variety evaluation by farmers; identifying and analyzing farmers' evaluations of crop characteristics and varieties; and dealing with social heterogeneity and other research issues. Topics covered included different statistical methodologies for analyzing data from on-farm trials; the mother-baby trial system, which is designed to incorporate farmer participation into research; the identification and evaluation of maize landraces by small-scale farmers; and a PPB process that aims to address the difficulties of setting breeding goals and choosing parents in diversity research studies. Summaries of the discussion, as well as the participatory breeding work currently conducted by the participants, are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.234 | 0.427 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".