A preliminary national analysis of some key characteristics of Canadian lakes
Bibliographic record
Abstract
Knowledge of Canada’s lakes is needed to manage environmental stresses. Lake inventory and lake feature databases were used to build a national impact assessment template and assess regional typology. There are ~910 400 lakes with area ≥ 0.1 km2(10 ha), 37% of the Earth’s total. Lake features (number of lakes by size class, maximum depth, mean–maximum depth ratio, Secchi depth, pH, and total dissolved solids) were modeled regionally by secondary watershed (SWS) using linear regression models. Lake trout ( Salvelinus namaycush ) occurrence was analyzed as a cofactor to highlight regional links between lake characteristics and aquatic biota. Significant (R2from 0.231 to 0.492) regional models were obtained using area or maximum depth, lake trout occurrence, and their cross products as covariates. Analyses of fitted SWS coefficients showed that ecozones were a better predictor of lake characteristics than primary watersheds. The national typology was consistent with previous regional assessments. The regional models were used to estimate the number, area, and volume of lake trout lakes by size class and ecozone. There are ~66 500 lake trout lakes covering ~3 510 000 km2primarily on Boreal and Taiga Shield areas. Regional lake resource models will enable national assessment of stresses such as climate change and invasive species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".