Spatial variability in growth and mortality of the red sea urchin, <i>Strongylocentrotus franciscanus</i>, in northern California
Bibliographic record
Abstract
Natural and fishing mortality rates of the red sea urchin, Strongylocentrotus franciscanus, in northern California were estimated from growth increment and size distribution data under the assumption of a constant recruitment rate. Mean asymptotic test diameter, standard deviation of asymptotic test diameter, growth rate coefficient, and natural mortality rate were first estimated for three nominally unharvested sites, Bodega Marine Reserve, Caspar Closure, and Salt Point. These estimated growth and mortality parameters differed among sites, leading to substantially different yield-per-recruit surfaces. Estimates of fishing mortality rate from size distributions collected at 11 harvested sites were then calculated based on the growth and natural mortality estimates obtained from the Caspar Closure and Bodega Marine Reserve sites. Estimates of fishing mortality rate ranged from 0.11 to 1.87·year-1. The alongshore pattern of fishing mortality rate was moderately correlated with landings and effort, but the spatial pattern of rare, strong recruitment events also appeared to influence values of fishing mortality rate. The positive bias in estimates of fishing mortality rate due to recruitment variability indicated that our observed pattern in estimated values for fishing mortality rate could have been caused by the historical spatial pattern of interannual variability in recruitment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".