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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".