Recommendations on the design of a Multispecies Benthic Marine Invertebrate Dive Survey Program for Stock Monitoring in British Columbia
Bibliographic record
Abstract
A new multispecies benthic invertebrate monitoring program is being developed to quantitatively monitor stock abundance over time on the British Columbia (BC) coast. This dive survey is designed to monitor abundance of Green (Strongylocentrotus droebachiensis), Red (Mesocentrotus franciscanus) and Purple (Strongylocentrotus purpuratus) Sea Urchin, Giant Red Sea Cucumber (Apostichopus californicus), Northern Abalone (Haliotis kamtschatkana), Sunflower Star (Pycnopodia helianthoides) and Pacific Geoduck (Panopea generosa) populations, and also to collect detailed habitat information on substrate and algae. The survey protocol was developed in 2016 and is described in detail. Pilot surveys were conducted in different areas of the coast from 2016 to 2021. Data from these pilot surveys, along with data from single-species surveys (1978 to 2021), were analysed to make recommendations on optimal survey design for the new monitoring program. The methods included reviewing single species analyses that informed sampling intensity on transects, looking at historic maximum transect lengths, investigating stratification variables, and using an acceptance sampling method to determine the minimum required number of transects, given predefined risks and probabilities associated with being above or below reference points. In addition, densities of the Giant Red Sea Cucumber and size and habitat subsets of Red Sea Urchin populations were estimated as an example of how these data could be used to assess stock status in the future. The recommendations on survey design are to: 1) Use the dive survey protocol described in Appendix A of the Research Document; 2) Exclude sections of shoreline with fetch values lower than 20,000 m or higher than 2.52 million m; 3) Ensure surveys occur at the same time of year to avoid introducing seasonal variation to the data; 4) Use the common (across species) coast wide standard deviation-to-mean ratio of density (animals per m2) equal to 1.27 in calculations to determine the target number of transects to be sampled; 5) Conduct at least 240 transects coast wide to estimate stock status; 6) Implement a two-stage, random sampling design that minimizes the time required to cover the entire BC coast and optimizes the efficient use of available resources; and 7) Continue to explore pre- or post-stratification variables to improve survey precision, as data become available.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".