Bay-scale habitat mapping of American lobster (Homarus americanus)
Bibliographic record
Abstract
Bay-scale habitat maps for benthic organisms, especially those of economically important species, are important for the comprehensive development of Marine Spatial Planning (MSP) initiatives. However, few such maps of that scale exist for the American lobster (Homarus americanus) in Canada's Maritime provinces. There are several coastal activities considered by MSP which occur at scales of less than 1 km (e.g. aquaculture, etc.), but which have limited spatial placement options because of the broadly binned or patchy lobster habitat data available. Therefore, the objective of this project is to develop a standard method of bay-scale lobster habitat mapping specifically for the purposes of adding scientific GIS layers to the MSP process of finfish aquaculture lease placement. Two bays in Nova Scotia and New Brunswick have been surveyed and mapped using acoustic data (backscatter from a single-beam echosounding system), ground-truth video, and various forms of lobster presence data. Through different procedures, the acoustic data have been split into substrate category bins and interpolated between tracks, then compared to the ground-truth video to create error matrices and provide estimates of accuracy. The accuracy of the maps created through these differing procedures has been evaluated and the early results of this work will discussed.
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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 | 0.001 | 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".