Biological and environmental drivers of deep-sea benthic ecosystem functioning in Canada’s Laurentian Channel Area of Interest (AOI)
Bibliographic record
Abstract
Ongoing environmental changes and accelerating biodiversity loss raise concern and interest about the role of environmental factors and biodiversity in determining marine ecosystem functioning. This study aims to identify the main drivers of benthic ecosystem functioning in deep-sea sedimentary habitats in the Laurentian Channel Area of Interest (AOI), and in particular the role of sea pens (Pennatulacea) as potential keystone species in the area. Using the ROV ROPOS we collected sediment cores and measured environmental variables from 6 stations inside the AOI (depths 348–445m) in September 2017. Through 48-hours incubations and flux measurements (oxygen, inorganic nutrients), we estimated organic matter remineralization, a key benthic function. Preliminary analyses show no significant variation in fluxes among stations, despite significant differences in environmental variables However, the presence/absence of Pennatulacea inside the cores indicated some capability to enhance remineralization and particularly nitrification. Ongoing analyses will address sediment properties, macrofaunal biodiversity, prokaryotic abundance, and biological traits as drivers of remineralization. Shedding new light on the primary drivers of ecosystem functioning in the area will inform the design or monitoring strategies proposed for this AOI and offer new perspectives and tools for MPA design.
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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 | 0.001 |
| 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 | 0.001 |
| 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".