PHENET webinar (22 Nov 24): Use Case of Doller Valley
Bibliographic record
Abstract
Presenter: Laurent Saint-Andre. This webinar is part of a series of webinars providing an overview of the activities within PHENET. It includes multiple speakers addressing a methodological section and the application of these methods in dedicated use cases. The methods section addresses the latest developments with respect to spatial and temporal variation in agricultural and forestry ecosystems providing a wider access to earth observation data. This method section is followed by use cases addressing relevant scientific questions such as first growth and dieback as a result of climate change as well as support of farmers based on historical data and actual measurements towards sustainable agricultural management. Target audience: Environmental scientists interested in plant sciences, natural and agricultural ecosystems covering the scale from single plants to landscapes from academic as well as non-academic organizations and farmers. Keywords: natural and agricultural ecosystems, remote sensing, agricultural management, first dynamics, plant phenotyping. Scientific subject: sustainable agriculture, forestry, agronomy ecology. 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.054 |
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".