Quantifying status and trends from monitoring surveys: application to pygmy whitefish (<i>Prosopium coulterii</i>) in Lake Superior
Bibliographic record
Abstract
Population assessments of fish species often rely on data from surveys with different objectives, such as measuring biodiversity or community dynamics. These surveys often contain spatial–temporal dependencies that can greatly influence conclusions drawn from analyses. Pygmy whitefish (PWF, Prosopium coulterii) populations in Lake Superior were recently assessed as Threatened by the Committee on the Status of Endangered Species in Canada, which motivated a thorough analysis of available data to improve our understanding of its population status. The US Geological Survey conducts annual bottom trawl surveys in Lake Superior that commonly capture PWF. We used these data (1989–2018) to model temporal trends in PWF biomass density and make lake-wide population projections. We used a Bayesian approach, integrated nested Laplace approximation (INLA), and compared the impact of including different random structures on model fit. Inclusion of spatial structure improved model fit and conclusions differed from models omitting random effects. PWF populations have experienced periodic fluctuations in biomass density since 1989, though 2018 may represent the lowest density in the 30-year time series. Lake-wide biomass was estimated to be 71.5 t.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".