Bibliographic record
Abstract
Canada holds several of the world's large lakes (⩾100 km2). Many of these lakes, apart from the largest like the Great Lakes, are almost unknown beyond their location and area. This study documents a recent compilation and analyses of some key limnological features of these lakes: drainage area, lake area, maximum and mean depth, pH, Secchi depth, and total dissolved solids. The analyses showed the relationships among these features and with their primary watershed and ecozone assignments. Lake area and maximum depth were good predictors of some of the other lake variables. Ecozone was generally a better predictor than primary watershed of regional variation in lake variables with lake area or maximum depth as a covariate scaling for lake size. To enable regional impact assessments of cumulative environmental pressures of Canada's large lakes, these predictive regression models provide a stop-gap means for estimating key lake characteristics when data are missing. However, as cumulative pressures increase, Canada needs to increase efforts to undertake limnological inventories and learn more first hand about these poorly known lakes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".