Hydrométrie 2017 : mesures et incertitudes : Bilan sur le colloque des 14 et 15 mars 2017 à Villeurbanne (France)
Bibliographic record
Abstract
This article presents the highlights of the Hydrométrie 2017 conference: origin of the participants, main advances on the three themes of the conference (issues related to measurement networks, measurement and methods in rivers and urban networks, qualification and valuation of data, analysis of uncertainty), scientific awarding (best poster, SHF Grand Prize). A Hydrometry section, based on the French Doppler Hydrometry group, has just been created at the SHF. / Cet article présente les éléments marquants du colloque Hydrométrie 2017 : origine des participants, principales avancées sur les trois thèmes de la conférence (enjeux liés aux réseaux de mesure, mesure et méthodes en rivières et réseaux urbains, qualification et valorisation des données, analyses d'incertitude), attribution de prix scientifiques (meilleur poster, grand prix de la SHF). Une section Hydrométrie, basée sur le groupe francophone Doppler Hydrométrie, vient d'être créé à la SHF.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".