Marine habitat mapping in support of Marine Protected Area management in a subarctic fjord: Gilbert Bay, Labrador, Canada
Bibliographic record
Abstract
Abstract This paper presents an approach that allows production of benthic substrate and habitat maps in fjord environments. This approach is used to support the management of the Gilbert Bay Marine Protected Area (MPA) in southeastern Labrador, Atlantic Canada. Multi-beam sonar-derived bathymetry, seabed slope, and acoustic reflectance (backscatter) were combined using supervised classification methods and GIS with ground-truthed benthic sampling in order to derive maps of the substrates and main benthic habitats. Six acoustically distinct substrate types were identified in the fjord, and three additional substrate types without a unique acoustic signature were recognized. Ordination by multidimensional scaling and analysis of similarity generalized these to four acoustically distinct habitat types. Greatest within-habitat (alpha) diversity was found in the coralline-algae encrusted gravel habitat. Greatest between-habitat (beta) diversity was found in the management Zones 1 and 2, which have the highest level of protection. The study confirmed that the zoning plan for the MPA, which was designed to protect spawning and juvenile fish habitat for a local genetically distinct population of Atlantic cod, afforded highest levels of protection to areas with highest habitat diversity.
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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 | 0.001 | 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".