Observational fish data for ten important fisheries species across the province of Québec [1970-2021]
Bibliographic record
Abstract
The data covers sampling sites over all of the province of Québec, with more data in the southern regions of the province. Data are from Hydro-Québec and the Québec Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP). These datasets include a heterogeneous mix of standardized and non-standardized fish surveys by government biologists and by environmental consulting firms sub-contracted by Hydro-Québec, collected between 1973 and 2021. Overall, 6498 unique sites (3087 sites in lakes, 3412 sites in rivers; reservoirs excluded), were included in the database. For each site, the data consisted of species counts (adult and juvenile life stages), location (latitude and longitude), sampling date, habitat type (lake or river) and fishing gear (three categories: electrofishing, gillnet, or seine). Climate data (means over 1970 to 2000) and elevation were extracted for each site according to the site’s location from the WorldClim website (https://www.worldclim.org/) in 2023; a site which provides global weather and climate data at high spatial resolution. All 19 ‘bioclimatic variables’ provided by WorldClim were obtained from WorldClim raster files (10-min. resolution) using the function extract from the package raster (Hijmans and van Etten 2012). Five weakly-correlated bioclimatic variables were retained: annual means for temperature and precipitation, mean diurnal temperature range, annual temperature range, and precipitation seasonality (coefficient of variation of monthly total precipitation).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".