Development and assessment of a new method for combining catch per unit effort data from different fish sampling gears: multigear mean standardization (MGMS)
Bibliographic record
Abstract
Fish community assessments are often based on sampling with multiple gear types. However, multivariate methods used to assess fish community structure and composition are sensitive to differences in the relative scale of indices or measures of abundance produced by different sampling methods. This makes combining data from different sampling gears and methods a serious challenge. We developed a method of combining catch per unit effort data that standardizes catch per unit effort data across gear types, which we call multigear mean standardization (MGMS). We evaluated how well MGMS and other types of standardization reflect underlying community structure through a computer simulation that generated model riverine-fish communities and simulated sampling data for two gears. In these simulations, combining sampling observations from two gears with MGMS produced community structure estimates that were highly correlated with true community structure under a variety of conditions that are common in large rivers. Our simulation results indicate that the use of MGMS to combine data from different sampling gears is an effective data manipulation method for the analysis of fish community structure.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".