Gradients spatio-temporels des substances pharmaceutiques et conséquences écologiques en cours d'eau agricole et urbain (PharmaTOX): Rapport final Action n°75 du Programme 2021 au titre de l’accord cadre Agence de l’Eau ZABR
Bibliographic record
Abstract
Remerciements:Les auteurs remercient l'ensemble des contributeurs cités ci-dessus, qui ont participé aux campagnes d'échantillonnage et/ou ont réalisé l'ensemble des analyses chimiques et biologiques présentées.Nous remercions également Lionel Navarro (Agence de l'eau RM&C), pour son accompagnement lors du montage et le suivi de ce projet, le Comité Intercommunautaire pour l'Assainissement du Lac du Bourget (CISALB) et en particulier Sebastian Cachera, responsable de la gestion des milieux aquatiques au sein de celui-ci, ainsi que le Syndicat Mixte des Rivières du Beaujolais (SMRB) et en particulier Alice Patissier et Grégoire Thévenet, respectivement chargée de mission qualité de l'eau et responsable du SMRB, pour leur participation active aux réunions de pilotage et de restitution et les informations fournies concernant les sites d'études.Annexe 1 : Liste des contaminants chimiques analysés dans le projet 48Annexe 2 : Caractéristiques physico-chimiques des stations 49Annexe 3 : Concentrations en caféine dans les eaux de surface et les sédiments 50
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".