Estimating seasonal Pacific geoduck clam <i>Panopea generosa</i> show-factors from long-term observation
Bibliographic record
Abstract
Abstract Objective The geoduck show-factor is the probability an individual geoduck is detectable to a diver during a stock assessment survey. It is one of the values used to estimate the abundance of geoducks. This paper presents estimates of show-factor according to data collected during 21 approximately monthly surveys over a 32-month period. The new estimates are compared against other long-term studies of show-factor and against estimates made from shorter term (approximately daily surveys over a period of approximately 1 week) studies. Methods Data were collected from three plots near Marina Island, British Columbia, and three novel methods were used for the analysis. Two of the methods estimate show-factors at the time of data collection, while the third method treats show-factor as a function of the time of year. All three methods generate probabilistic results. Result Each method of show-factor analysis indicated a strong seasonal effect. Show-factors were highest from March to June and lowest from October to December. The difference between the high and low show-factors was at least a factor of two. The three methods generated estimates of show-factor that are generally lower than the values previously used as part of stock assessment on the coast of British Columbia. Conclusion If this difference between long- and short-term show-factor can be generalized to the many commercial beds on the coast of British Columbia, the abundance of geoducks has been underestimated and the fishery is even more cautious than currently believed. As of yet, long-term data have not been collected on a coastwide basis and conclusions cannot be more definitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".