Biogenic structures in the Arctic: an ecosystem functioning hotspot?
Bibliographic record
Abstract
In deep-sea environments, resources availability and habitat complexity drive the distribution of benthic organisms. Biogenic structures such as cold-water corals and sponges create a three-dimensional habitat that facilitate sediment and resources accumulation and therefore show a high abundance of the associated species compared to bare sediments. However, the functions of these biodiversity hotspots in the ecosystem functioning are still poorly known. In this study, we addressed three main questions: 1) do benthic fluxes vary according to their position within patches and bare sediment? 2) are infaunal communities similar in biogenic structure and bare sediment patches? and finally, 3) which variables explain benthic fluxes in these patches? Infaunal communities and benthic fluxes were examined in Arctic regions presenting two types of biogenic structures: corals ( Keratoisis sp.) and arborescent sponges. To compare ecosystem functioning between the biogenic structure versus bare sediment patches, sediment cores were collected to quantify benthic fluxes (nitrate, nitrite, ammonium, phosphate and silicate) and the diversity, abundance and composition of infauna. Multivariate analyses suggested that biogenic structure and bare sediment patches exhibited different infaunal assemblage and a spatial pattern for the benthic fluxes even with a distance of 100 m between the type of patches.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".