Data Quality, Data Quantity, and Its Effect on an Applied Stock Assessment of Cisco in Thunder Bay, Ontario
Bibliographic record
Abstract
Abstract Stock assessments, or population models developed to support fishery management decisions, require informative data to produce reliable estimates. However, resources available to collect these data are limited. Thus, information relating the effects of different data collection schema on stock assessment performance should be of interest to fishery managers. We used an existing data set on a stock of Cisco Coregonus artedi in Thunder Bay to simulate various degrees of reduction in available data. We considered both cluster subsampling of biological data from the commercial fishery harvest (which determine the observed harvest age composition) and reductions in the frequency of hydroacoustic surveys in order to examine their effect on fits of an age-structured stock assessment model for the stock. Our results indicate that reductions in the frequency of hydroacoustic surveys would have a greater effect on applied stock assessment performance for Thunder Bay Cisco than would reductions in biological sampling to randomly selected temporal clusters of the fishery harvest. Reduction in the frequency of the hydroacoustic survey resulted in different point estimates and larger estimated uncertainty for spawning biomass and natural mortality rate compared with the original assessment model. This was likely largely driven by increases in lag between the final year of the survey and the current year of the assessment. The lower influence of reduced biological sampling may be due to the highly variable nature of Cisco recruitment, where large or “boom” year-classes were still evident in the reduced biological samples, combined with information from survey age compositions. We suggest a priority be placed on performing hydroacoustic surveys with some regularity, such that when they are performed, they are done extensively to minimize uncertainty (measurement error). The data subsampling approach used here could be used in many assessments to determine if a reduction in sampling of various types could be implemented without materially changing assessment results.
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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 | 0.018 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".