Geoduck<i>Panopea generosa</i>Growth at Its Southern Distribution Limit in North America using a Multimodel Inference Approach
Bibliographic record
Abstract
The Pacific geoduck Panopea generosa is distributed throughout the North Pacific temperate zone from Alaska to Baja California and is described as a species that reaches large sizes, has prolonged longevity, and exhibits slow growth. This study assessed the individual growth and population structure of the P. generosa population located at its southernmost geographic distribution limit. Shell length and total weight data were obtained from a commercial fishery established on Punta Canoas, Baja California. Individual age was determined by counting growth lines for 243 organisms. The results revealed the following averages: shell length (SL), 113.5 mm; total weight, 511.8 g; age, 12.5 y. The relationship of SL to total weight showed negative allometric growth (b = 2.16). Size-at-age data were adjusted to von Bertalanffy, Gompertz, logistic, Johnson, and Schnute growth models according to the multimodel inference (MMI) approach. The best candidate growth model was selected based on the Akaike information criterion (AIC) and the Schwartz—Bayesian criterion (SBC). The AIC indicated that the Schnute growth model was the best candidate growth model, whereas the SBC showed the Johnson growth model was best. These growth models indicate that between 7 y and 8 y of age, organisms reach 75% of their estimated asymptotic length (SL, ∼103 mm), and although the growth rate decreases subsequently, growth continues up to 25 y (maximum age observed). The MMI approach applied to the analysis of growth in Panopea species identified particular population attributes that are not observable via the von Bertalanffy model. The population of P. generosa from Punta Canoas exhibited smaller mean SL, lower mean weight, an age structure with fewer age classes, and slower growth when compared with northern populations in Washington state and British Columbia, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".