Physico-chemical and morphometric data from the Stage de Limnologie at at the Station de Biologie des Laurentides (SBL), Université de Montréal
Bibliographic record
Abstract
HeHere we provide three datasets that combine profiles of oxygen, pH, conductivity, water temperature and light; the average concentrations of Chlorophyll a, Total Phosphorus (TP) and water color for each strata; basic morphometric features such as average depth, maximum depth, lake volume, lake area and watershed area for 6 lakes in the Laurentian region of Quebec in close proximity to, or at the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Original data come from samples collected during the undergraduate field course in limnology between 2016 and 2018 in late summers.re we provide three datasets that combine profiles of oxygen, pH, conductivity, water temperature and light; the average concentrations of Chlorophyll a, Total Phosphorus (TP) and water color for each strata; basic morphometric features such as average depth, maximum depth, lake volume, lake area and watershed area for 6 lakes in the Laurentian region of Quebec in close proximity to, or at the Station de Biologie des Laurentides (SBL), the Université de Montréal field station. Original data come from samples collected during the undergraduate field course in limnology between 2016 and 2018 in late summers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.047 |
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".