Biomass estimates for Giant Red Sea Cucumber (Apostichopus californicus, Stimpson, 1857) as determined through surveys conducted from 2014 to 2020
Bibliographic record
Abstract
Stock assessment surveys of the Giant Red Sea Cucumber, Apostichopus californicus (Stimpson, 1857), population in British Columbia have been ongoing since 1998. Between 2014 and 2020, 11 surveys were conducted to provide Fishery Managers with population density and biomass estimates. This report provides survey-based density, weight, and biomass estimates for each of the analysis areas covered by the surveys. Average sea cucumber densities ranged from 0.2 to 20.2 c/m-sh in the analysis areas, while average split weights ranged from 144 to 379 g. In general, the Knight Inlet/Allison Harbour, East Barkley Sound and West Barkley Sound survey results showed low sea cucumber densities, while the Kitimat Arm/Gardner Canal, South Dean Channel, Area 13C and Kyuquot Sound surveys showed moderate sea cucumber densities and the South Burke Channel, Area 13A, Finlayson and Nootka Sound surveys showed high sea cucumber densities, relative to commercial and respective regional baseline densities (Duprey et al. 2011), however patterns of sea cucumber densities for some surveys were more complex to interpret. The relative abundances of ad hoc observations of Geoducks (Panopea generosa), Red Sea Urchins (Mesocentrotus franciscanus), and Green Sea Urchins (Stronglyocentrotus droebachiensis) were also estimated and are presented here.
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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 | 0.002 | 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) | 0.001 | 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".