Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic
Bibliographic record
Abstract
Abstract Beazley, L. I., Kenchington E. L., Murillo, F. J., and Sacau, M. 2013. Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic. – ICES Journal of Marine Science, 70: . The influence of structure-forming deep-water sponge grounds on the composition, diversity, and abundance of the local epibenthic megafaunal community of the Flemish Pass area, Northwest Atlantic was statistically assessed. These habitats are considered vulnerable marine ecosystems and, therefore, warrant conservation measures to protect them from bottom fishing activities. The epibenthic megafauna were quantified from four photographic transects, three of which were located on the western slope of the Flemish Cap with an overall depth range of 444–940 m, and the fourth in the southern Flemish Pass between 1328 and 1411 m. We observed a diverse megafaunal community dominated by large numbers of ophiuroids and sponges. On the slope of the Flemish Cap, sponge grounds were dominated by axinellid and polymastid sponges, while the deeper sponge ground in the southern Flemish Pass was formed mainly by geodiids and Asconema sp. The presence of structure-forming sponges was associated with a higher biodiversity and abundance of associated megafauna compared with non-sponge habitat. The composition of megafauna significantly differed between sponge grounds and non-sponge grounds and also between different sponge morphologies. Surface chlorophyll a and near-bottom salinity were important environmental determinants in generalized linear models of megafaunal species richness and abundance.
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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 | 0.000 | 0.000 |
| 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".