Coldwater fish oxythermal habitat in Minnesota lakes: influence of total phosphorus, July air temperature, and relative depth
Bibliographic record
Abstract
An empirical model was developed that describes the influence of lake productivity, climate, and morphometry on coldwater fish oxythermal habitat. An oxythermal habitat variable called temperature at 3 mg·L–1 of dissolved oxygen (TDO3) was developed by interpolating the water temperature at a benchmark oxygen concentration (3 mg·L–1) from a temperature–oxygen profile. Coldwater habitat was most available in the least productive lakes (total P < 25 µg·L–1) with the greatest relative depths (geometry ratios < 2 m–0.5) and where mean July air temperatures were less than 17 °C. Species response curves were developed from values of TDO3 measured during the greatest period of oxythermal stress in late summer (maxTDO3). Lake trout was present in lakes with the lowest values of maxTDO3, while cisco was present in lakes with the highest and broadest range of maxTDO3. Projections for a scenario where climate warming (+4 °C in mean July air temperature) was accompanied by eutrophication (doubling of total P) indicated that coldwater fish oxythermal habitat would be devastated in a subset of lakes typical for Minnesota. Protecting deep, unproductive lakes from eutrophication will be a necessary management strategy to ensure that coldwater fish persist in at least some Minnesota lakes after climate warming.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".