PHENET webinar (6 Dec 24): Model-assissted wheat phenotyping
Bibliographic record
Abstract
Presenter: Raul Lopez-Lozano. In this webinar we address questions related to the interactions between crops and environment with AI based tools for envirotyping and phenotyping used in a specific use cases with the goal to predict wheat yield by identifing functional traits of different genotypes and, assess the apple tree physiological and health status in contrasting environments. Target audience: Envirotyping and phenotyping community in academic as well as non-academic organizations. Keywords: plant phenotyping, envirotyping, orchard, wheat, AI based tools. Scientific subject: sustainable agriculture, plant breeding, remote sensing, agronomy. Project title: Tools and methods for extended plant PHENotyping and EnviroTyping services of European Research Infrastructures. Funding agency/agencies: European Commission, Grant agreement ID: 101094587, Award Period: 2023-2027. CORDIS website: https://cordis.europa.eu/project/id/101094587
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.230 |
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".