Associations of lobsters (Homarus americanus) off southwestern Nova Scotia with bottom type from images and geophysical maps
Bibliographic record
Abstract
Abstract Tremblay, M. J., Smith, S. J., Todd, B. J., Clement, P. M., and McKeown, D. L. 2009. Associations of lobsters (Homarus americanus) off southwestern Nova Scotia with bottom type from images and geophysical maps. – ICES Journal of Marine Science, 66: 2060–2067. Images from an underwater towed vehicle (Towcam) were used to estimate densities and to evaluate bottom-type associations of lobsters (Homarus americanus), crabs (Cancer spp.), and scallops (Placopecten magellanicus). Images were obtained in October 2006 along 14 line-transects off southwestern Nova Scotia in an area with productive lobster and scallop fisheries. Lobsters were observed in 4% of the 2044 images, crabs in 7%, and scallops in 40%. On sand, gravel, and cobble seabed, lobsters were readily observable. On rougher substrata with boulders, some lobsters were still evident either in the open or partially hidden in shelters. Estimated densities of lobsters from the images on some transects were 0.04 m−2, approximately half of the estimates of lobster density for adjacent inshore areas from scuba, but 34 times higher than estimates from scallop drags in the same area. Models of animal presence by bottom type were evaluated with categories that were (i) geophysically based (map of bottom type from geophysical characteristics) and (ii) image-based (sediment size from images). Significant relationships were evident with both types of seabed categorization, suggesting that it would be beneficial to stratify surveys using geophysical categories. Depth was also significant in determining presence/absence of lobsters and crabs. There is potential to develop indicators of lobster abundance using underwater imaging, and stratification by bottom type should be incorporated into surveys.
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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 | 0.001 |
| 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) | 0.002 | 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".