Alaska deep-sea coral and sponge assemblages are well-defined and mostly predictable from local environmental conditions
Bibliographic record
Abstract
Vulnerable marine ecosystems (VMEs), including deep-sea corals and sponges, are important habitats for many fish and invertebrate species and are at risk from the effects of fishing, seafloor mining, and climate change. We describe the zoogeography of deep-sea corals and sponges in Alaska, USA, and identify the environmental factors structuring these assemblages. Images were collected with a calibrated stereo drop-camera (n = 853 transect locations). We used cluster analysis to identify assemblages, canonical correspondence analysis to identify the primary environmental variables structuring these assemblages, and random forest and generalized additive modeling to predict their spatial distributions. The 6 identified assemblages were well defined, with each dominated by a single indicator taxon (2 coral taxa: Primnoidae, Stylasteridae; 2 sponge taxa: Demospongiae, Hexactinellida; 2 sea whip/pen taxa:Balticinasp.,Ptilosarcus gurneyi). The most common assemblages were Demospongiae, Primnoidae, andBalticinasp. Primnoidae and Demospongiae were positively influenced by greater maximum tidal current, bottom current, and bottom temperature as well as proportion of rock and cobble (high for Primnoidae; low to medium for Demospongiae).Balticinasp. was influenced in the opposite direction and was aligned along lower maximum tidal current, bottom current, and bottom temperature as well as unconsolidated sediment and greater depth. We defined VME community indicators as the 6 assemblages, each dominated by a single indicator taxa. These VME community indicators can guide the identification of protected areas.
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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 | 0.001 | 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".