Incorporating spatial analyses into conservation and monitoring of deep-sea megafauna in Marine Protected Areas (MPAs)
Bibliographic record
Abstract
Deep-sea ecosystems are being impacted by anthropogenic stressors, such as trawling and oil-gas exploration. Protection of these ecosystems is delayed by limited understanding of spatial distribution, suitable habitat, species associations, and recruitment. Imagery was analyzed from the Laurentian Channel AOI and 3 canyons (Corsair, Georges, Fiddlers Cove) on the western Scotian Slope in the Northwest Atlantic Ocean. We used two sampling designs, exploratory linear transects and a systematic-cluster transect array and will compare the information that can be extracted from each method. Megaepifaunal biodiversity, abundance, and species-species associations were identified at each site. For example, at Fiddlers Cove, different types of Gorgonian corals (e.g. Acanella, Desmophyllum , and stoloniferous coral), soft corals, and sponges occurred mainly on outcrops; sea pens and anemones, along with large colonies of Paragorgia arborea were present in Corsair Canyon; and several Gorgonian corals, anemones, lobsters, and Holothuroidea were present in Georges Canyon. We will use spatial analyses to measure spatial structure at local and regional scales, identify species-environment associations, and predict suitable habitat for deep-sea megaepifauna. Overall, the study will provide a broader understanding of deep-sea megaepifaunal ecosystems, and develop recommendations for a deep-sea MPA monitoring framework to achieve effective conservation that promotes biodiversity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".