Cold-water coral distributions and surficial geology at five spatial scales on the Flemish Cap, northwest Atlantic
Bibliographic record
Abstract
Cold-water coral (CWC) distributions are strongly influenced by substrate, as they are sessile, long-lived benthic species. While the attachment substrate preferences (hard vs. soft) of many coral species are well known, the relationships between coral species distributions and geological environments are less clear. This study investigates the relationship between CWC distribution and geological environments in a deep offshore setting, examining both surficial geology and bedrock lithology. In 2010 we used a remotely operated vehicle to conduct four video surveys on the southern and eastern flanks of the Flemish Cap, NW Atlantic, ranging in depths from 870 m to 2900 m. CWC were identified to the lowest possible taxonomic level and assigned to functional groups: large gorgonians, small gorgonians, soft corals, pennatulaceans, antipatharians, Desmophyllum dianthus, and reclining solitary scleractinians. Surficial geology was classified at five spatial scales (10 m, 50 m, 100 m, 500 m, 1000 m) along each transect into one of six surficial geological and/or lithological geological facies (fine grained sediment, gravelly fine grained sediment, fine grained sediment and bedrock, igneous bedrock, and sedimentary bedrock). A total of thirty CWC species were observed, with each transect displaying a unique species composition and surficial geology. Functional groups were represented on most facies and depths. Anthomastus sp. (soft coral) was the most abundant coral species observed, and was found on most facies and depths. Soft corals and large gorgonians were the most abundant on the gravelly fine grain and sedimentary bedrock facies between 1673-1873 m. Analysis of Similarity (ANOSIM) showed an influence of both facies and depth on coral species composition, with depth apparently more important. Geological facies had a significant difference on coral species composition when measured at finer scales (10 m, 50 m, 100 m, and 500 m) but not at broader scales (1000m). Of the fine scales, 100 m was the most significant for both CWC species and functional groups. Our results suggest that bathymetry and oceanography are dominant influences on coral distribution at broad scales, with surficial geology dominating distributions at scales finer than 1 km.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".