Multi-species fisheries-independent survey for Great Slave Lake
Bibliographic record
Abstract
A lake-wide depth-stratified summer fish survey using multi-mesh experimental gillnets was developed as a fishery independent sampling program for Great Slave Lake. To support assessments of stock productivity, a suite of biological data (e.g., fish length, weight, sex, condition, health attributes) and samples (e.g., ageing structures, stomachs, genetic samples) will be collected for individual fishes. Otoliths, pectoral fin rays, and scales will be collected for fish ageing. Otoliths are the preferred ageing structure, and it is unclear whether fin rays or scales are the preferred alternate; additional research on ageing accuracy and consistency with fish fin rays and scales in Great Slave Lake is needed. In addition to fish sampling, zooplankton nets and benthic grabs will be used at each site to provide data on lower trophic levels to support ecosystem assessments. Environmental data (e.g., depth profiles for dissolved oxygen, chlorophyll a, pH, water temperature and turbidity, weather and wave conditions) will be recorded at each site. This survey is Fisheries and Oceans Canada’s first ecosystem-level survey program for Great Slave Lake and was developed without previous estimates of variability or species distributions; therefore, the sampling program will need to undergo a preliminary review after three years and a full review after five years. To establish empirical relationships between additional environmental variables and stock production: Water samples should be collected for nutrient analyses so that probe-derived chlorophyll a data can be related to phytoplankton composition. Secchi disc and water colour measurements should be taken at all stations to allow comparison with historical data. Data could be obtained from existing weather buoys and temperature loggers could be deployed on fishing nets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".