Evaluation of oxythermal metrics and benchmarks for the protection of cisco (<i>Coregonus artedi</i>) habitat quality and quantity in Wisconsin lakes
Bibliographic record
Abstract
The cisco (Coregonus artedi) requires cold, well-oxygenated water and is threatened by climate warming and lake eutrophication, especially at the southern edge of its range in the Laurentian Great Lakes region. In this paper, a method is described to assess the quality, quantity, and variability of suitable cisco habitat. Two metrics are proposed to characterize existing cisco oxythermal habitat in this region: water temperature in the water column at a dissolved oxygen of 6 mg/L (TDO6) and “cisco layer thickness” (CLT), the vertical extent of the water column within which water temperature is ≤22.8 °C and dissolved oxygen is ≥6 mg/L. It is proposed that values of TDO6 = 22.8 °C and CLT = 1 m be used as benchmarks to identify cisco lakes with potentially stressful oxythermal habitat. Long-term summer data from 14 Wisconsin cisco lakes with a wide range of limnological characteristics indicated that TDO6 varied from 4.2 to 27.5 °C and CLT from −6.2 to 66.7 m. Cisco abundance across lakes was greater at lower values of TDO6 and higher values of CLT, and non-attainments of the proposed benchmarks were most likely in lakes with remnant or extirpated cisco populations. Non-attainment of benchmarks could occur from late June through September, and three or more samples across the entire summer would be required to have a reasonable probability of detecting a non-attainment in TDO6 or CLT within any given year. Across years, to detect a single year of non-attainment with an 80% level of confidence, two years of sampling would be sufficient for CLT and three years for TD06; to detect more than one non-attaining year, three years of sampling for CLT and six years of sampling for TD06 would be required. Despite relatively high temporal variation in some lakes, the proposed metrics and benchmarks appear to have value for assessing cisco oxythermal habitat in the Great Lakes region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".