Canadian Imaging And Sampling Technology For Studying Marine Benthic Habitat And Biological Communities
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The systematic mapping of marine benthic habitat and biological communities requires specialized oceanographic instrumentation. During the past ten years, as part of research programs investigating the effects of mobile fishing gear and offshore hydrocarbon development, Canadian scientists and engineers have developed a suite of tools for imaging and sampling seabed habitats over different spatial scales. Towcam is a towed vehicle which collects continuous but low-resolution video imagery of the seabed over a large area (i.e. 1-10 km transects). Campod is an instrumented tripod equipped with two video cameras and a 35-mm camera with 250-frame capacity. It is deployed while the ship is on station, or slowly drifting, and collects both general reconnaissance video and high-resolution imagery from a small area of the seabed. A hydraulically operated videograb, which uses the same conductor cable and winch as Campod, collects sediment and organisms from an area of 0.5 m2. Video cameras allow the operator to select the exact area of seabed to sample and to ensure that the grab closes properly. These three instruments are briefly described and examples of their application on the continental shelf off eastern Canada provided. These and comparable tools used by other ICES countries, when used in conjunction with acoustic survey tools (multibeam, seismic, sidescan, RoxAnn, QTCview, etc.), make possible the classification and mapping of marine benthic habitat and biological communities over large areas.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".