Biological characteristics of inland Lake Whitefish populations in Ontario
Bibliographic record
Abstract
Abstract Objective To assess the biological characteristics of Lake Whitefish Coregonus clupeaformis within inland lakes in Ontario at multiple scales and test whether there have been any changes in relative abundance, measured by catch per unit effort, spatially and temporally over 15 years. Methods A Broad-scale Monitoring Program, which uses a standardized random sample of the fish assemblage, has been conducted within inland lakes in Ontario since 2008 in roughly 5-year cycles on about 750 lakes. Lake Whitefish attribute and catch per unit effort data were used to assess variation in characteristics across the landscape. Result From 2008 to 2022, 54,941 Lake Whitefish were sampled among 524 different water bodies. Lake Whitefish relative abundance varied among fisheries management zones (FMZs), but not across cycles within or among FMZs. Relative abundance of Lake Whitefish was greater in lakes with lower large-bodied fish species diversity, greater Secchi depth, mean depth, and higher levels of hypolimnetic dissolved oxygen. Their relative abundance was greatest in the 12–35-m depth strata. Growth potential, age, and length at 50% maturity were similar between the sexes at the provincial and FMZ scales. Mean annual survival for populations with at least 50 Lake Whitefish sampled was 87%. Conclusion The demographics and relative abundance of Lake Whitefish throughout inland lakes in Ontario was indicative of limited exploitation on these populations at a landscape scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".