Modelling ice cover, timing of spring stratification, and end-of-season mixing depth in small Precambrian Shield lakes
Bibliographic record
Abstract
From a 3-year study of 21 northern Ontario lakes, models were developed to determine the duration of ice cover, predict the timing of spring stratification, and predict the end-of-season mixing depth. The model to determine lake freeze and thaw dates was based on the daily variability of water temperature measured with a data logger suspended 1 and 2 m below the surface. The model to predict the duration of time (days) from lake thaw to lake stratification was developed using the mean May air temperature (degrees Celsius), dissolved organic carbon, and lake surface area (r2 = 0.79). The end-of-season mixing depth was best predicted using days to stratification and dissolved organic carbon concentration (r2 = 0.72). By applying a simple climate change scenario model, we were able to show that increased air temperature, rather than increased water clarity, was the most important factor affecting the timing of stratification. In contrast, lake clarity was the most important factor affecting end-of-season mixing depth in small Shield 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".