Bibliographic record
Abstract
Numerical responses were examined in the movement of trawlers among seven aggregate fishing areas off the British Columbia coast. Three hypotheses for movement patterns were tested: (1) Movement follows traditional patterns, (2) Movement equalizes the gross dollar returns to effort (LPE$) in each area, and (3) Movement maintains relative LPE$ in each area. On the interannual time scale, I rejected the Equalize LPE$ hypothesis, but failed to reject either the Traditional Patterns or Maintain Relative LPE$ hypothesis. On the intra-annual time scale, I rejected both LPE$ hypotheses, but was unable to reject the Traditional Patterns hypothesis, although traditional movement patterns were evidently being modified by changes in the timing of LPE$ in all the areas and by fishing regulations in two of the seven areas. Three assumptions of the Equalize LPE$ hypothesis were violated, and accounting for violations (especially assumptions concerning movement and areas specific costs) would result in smaller differences in LPE$ between areas and between years within areas. Numerical responses have implications for anticipating the responses of fishing fleets to changes in regulations and for evaluating changes in the economic benefits of alternative fishing areas. Functional responses were examined in a multispecies fishery in Hecate Strait. Two similar techniques for estimating the abundance of exploited fish populations were compared: (1) Virtual Population Analysis (VP A) and (2) Catch-at-age analysis with auxiliary information (CAGEAN). Estimates for each of three species (Pacific cod, English sole, and rock sole) from both techniques were sensitive to the choice of natural mortality rate but insensitive to the choice of fishing mortality rate. In most cases, abundance and catchability estimates from alternative input parameters were highly correlated and had very similar time trends. Similar time trends in estimates were also obtained from the two techniques when the best estimates of input parameters were used. Estimates of abundances, catchabilities and catch per predator from both techniques were used to examine functional responses of trawlers to fish abundance. I compared the fit of three alternative single species functional response models: (1) a linear model (type I), (2) a saturating model (type II), and (3) a generalized equation that could mimic four different responses (types I-IV) depending on its parameter values. The generalized equation predicted sigmoid (type III) functional responses for 11 of the 12 data sets. However, only 5 of the model comparisons were statistically significant; all five indicated that a sigmoid response was most consistent with the data. Single species mortality models Fit most data sets poorly, although in most cases, the form of the mortality curve was consistent with the corresponding functional response. I also fit two alternative multispecies functional response models and examined one mechanism, switching, that could result in type III responses. Multispecies functional response and mortality models often resulted in much better fits than single species models for each species. I failed to detect switching in any of the data sets, although the power of the tests of the switching hypothesis was low in most cases. Type III functional responses and multispecies functional responses have implications for: (1) interpretations of abundance indices based on catch per unit of fishing effort, (2) equilibrium yield vs. abundance or harvest rate relationships and (3) simulation models used to evaluate alternative harvest strategies.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".