Whale falls as chemosynthetic refugia: a perspective from free-living deep-sea nematodes
Bibliographic record
Abstract
Whale carcasses create habitats in the deep sea which are colonized by organisms related to other chemosynthetic environments suggesting that whale falls may act as intermediate refugia for the dispersal and evolution of deep-sea organisms. Such evidence comes mainly from macrofaunal organisms whereas for the smaller meiofauna, data on whalebone assemblages is lacking. In this study, we investigated nematode colonization of whalebones experimentally deployed at 1500 and 3300 m depth and bones from a natural whale carcass found at 4204 m, in the Southwest Atlantic Ocean, off Brazil. By comparing whalebone assemblages to other deep-sea environments, we tested the hypothesis that whale fall nematode assemblages are more similar to those from other cognate chemosynthetic-based habitats, rather than those from sediments surrounding the carcass or other deep-sea habitats. Our results showed that whalebone nematode assemblages resemble those of hydrothermal vents and cold seeps and suggest that organic falls may act as intermediate refugia for meiofauna from chemosynthetic environments. It also showed that oceanographic conditions and the age of the carcasses on the ocean floor could influence nematode assemblage composition and richness. Such findings highlight the importance of organic falls for understanding the connectivity and phylogeny of benthic organisms, including representatives of the meiofauna.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".