DESCRIBING NORTHERN ABALONE, HALIOTIS KAMTSCHATKANA, HABITAT: FOCUSING REBUILDING EFFORTS IN BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
The northern abalone is listed as threatened under the Canadian Species at Risk Act. Northern abalone occur in a wide range of habitats from fairly sheltered bays to exposed coastlines. However, not all habitats are likely to support high abalone densities with large northern abalone that have high fecundity. Therefore, habitats that can support dense concentrations of large abalone would be better suited for aggregation rebuilding projects. Several experimental rebuilding projects are currently underway; the experimental sites were, in general, selected based on abalone presence and relative abundance. This study attempts to describe abalone habitat suitable for rebuilding efforts by using data from surveys completed at the start of the large decline of abalone densities observed in British Columbia (BC). Several areas were surveyed to determine abalone density on the southeast coast of the Queen Charlotte Islands and the north central mainland coast of BC between 1978 and 1980. Habitat data were recorded after each dive, including substrate types and dominant algae cover and species. Four categories of algal types were analyzed based on height and growth patterns: (1) canopy; (2) understorey (large bottom cover); (3) turf (short bottom cover); and (4) encrusting. In addition, an index of wave exposure was also calculated for each site surveyed. Northern abalone density was inversely correlated to mean abalone shell lengths. The exposure index was correlated positively to abalone density but negatively to mean shell length. Regression tree classifications successfully separated habitats of high and low abalone densities, but these differed from habitats classified using mean shell length as the response variable. To optimize rebuilding efforts, a compromise between the two classification models, one with density as the response variable and the other with mean shell length, may have to be developed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".