Predicting open-water thermal regimes of temperate North American lakes
Bibliographic record
Abstract
Temperature profoundly affects the physical, chemical, and biological attributes of lakes and is influenced by several abiotic factors. Lake temperature modelling permits regional estimates of seasonal fish thermal habitat availability; however, this requires models that are accurate across large spatial scales. To address this, we fit a semi-mechanistic seasonal temperature-profile model (STM) to 369 morphometrically diverse North American lakes with data spanning 1971–2016. STM with a fixed-depth thermocline formula accurately modelled lake temperature (median pseudo-R2: 0.95, median lake-year-specific root mean square error (RMSE): 1.13 °C). We used random forests to select candidate predictors, then used linear mixed-effects modelling, based on these predictors, to create empirical equations to predict STM parameters from lake-specific morphometric and climate measures. We tested the accuracy of our equations by predicting thermal profiles in 776 Ontario lakes, finding good agreement between predicted and observed temperatures (median lake-year-specific RMSE: 2.38 °C) and stratification occurrence (91.7%). These findings enhance our understanding of the factors that influence lake temperatures and can be used to identify lake types and regions that may be especially susceptible to climate change.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".