Bibliographic record
Abstract
Here we provided a dataset that combines the average concentrations of Chlorophyll a, dissolved organic carbon (DOC), transparency and basic morphometric features such as volume, average depth, and maximum among others for 238 lakes in the Laurentian region of Quebec in close proximity to the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Original data come for the Réseau du surveillance volontaire des lacs (RSVL) of the MECCL (Louis Roy), where average concentrations represent the summer time means collected between 2008-2017; number of years used to derive these means varies per lake. Morphometric characteristics come from the Blue Laurentides initiative, a joint activity between Richard Carignan (former professor at Université de Montréal and SBL Director) and the Conseil régional de environnement (CRE) des Laurentides (Anne Léger and Mélissa Laniel). Data are made available to consult for site selection for future research only.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.037 |
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".