What Can We Learn about Mortality from Sex Ratio Data? A Look at Lumpfish in Newfoundland
Bibliographic record
Abstract
Abstract Sex ratio data can provide information about mortality rates, which may be especially useful for sex‐specific fisheries when little other information is available. In this paper, we develop a theory and methods relating sex ratio to mortality rates and apply them to data for lumpfishCyclopterus lumpuson the southern Newfoundland Shelf. If the sexes have different mortality rates, and the rates as well as recruitment are constant with age and time, the sex ratio of the population provides an estimate of the ratio of the instantaneous mortality rates of the sexes. If a cohort can be followed over time, changes in the sex ratio provide an estimate of the ratio of the survival rates of the sexes or the difference in the instantaneous mortality rates. With the latter approach, it is not necessary for the sexes to have equal catchabilities in the survey nor for recruitment to be constant over time. This approach involves fewer assumptions and is of greater general interest, but it requires that cohorts be identified either by aging or tagging the fish. For lumpfish, we used sex ratio data from a research survey to estimate the change in the mortality rate of females, which are the subject of a sex‐specific roe fishery. Unfortunately, it is not possible to follow cohorts of lumpfish over time because lumpfish caught in the survey are not aged. Nonetheless, the sex ratio (females : males) decreased progressively from approximately 2.24 in 1985 to 1.09 in 1994. We attribute the decline to a doubling of the mortality rate of female lumpfish caused by the fishery, and our analysis probably underestimates the true change because of failures of assumptions. Our results are consistent with those of other work on lumpfish in Newfoundland and indicate that there is cause for serious concern about the effects of the fishery on lumpfish stocks.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".