Mapping seafloor habitats in the Bay of Fundy to assess megafaunal assemblages associated with Modiolus modiolus beds
Bibliographic record
Abstract
Dense aggregations of horse mussels exist in the Bay of Fundy and are thought to be associated with high biodiversity compared to surrounding habitats. Previous research show correlations between these aggregations and long narrow flow-parallel bedforms. In this study, high resolution seafloor photographs were compared to multibeam echosounder data to investigate benthic community and biodiversity patterns across the Bay of Fundy. Biophysical seafloor types (benthoscapes) were mapped with 71% accuracy using Object Based Image Analysis (OBIA) with fuzzy c-means classification. Community patterns were found to shift gradually across benthoscapes and mega-epifaunal biodiversity was highest in areas of mixed sediments and silty gravel and anemones. Horse mussels occurred mainly within the sand benthoscape where the flow parallel bedforms exist. Community patterns were also assessed at fine scales (5 m) using a by-eye interpretation of the flow parallel bedforms, where gradational shifts across the bedforms and surrounding areas were evident and abundance of horse mussels were found to be significantly greater on the bedforms. These findings will ultimately facilitate decisions around fisheries management, marine spatial planning, and monitoring of the horse mussel habitats and the surrounding benthoscapes within the Bay of Fundy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".