Multi-year seabed environmental baseline in deep-sea offshore oil prospective areas established using microbial biodiversity
Bibliographic record
Abstract
Microorganisms are the ocean's first responders to marine pollution events, yet baseline studies rarely focus on microbial communities. Temporal and spatial microbial biodiversity baselines were established using bacterial 16S rRNA gene amplicon sequencing of seafloor sediments in a deep-water oil prospective area along the Scotian Slope off Canada's east coast sampled during 2015-2018. Bacterial diversity was generally similar in space and time, with members of the family Woeseiaceae detected consistently in >1 % relative abundance, similar to seabed sediments in other parts of the world. Anomalous biodiversity results at one site featured lower Woeseiaceae as well as higher levels of bacterial groups specifically associated with cold seeps such as Aminicenantes. This was unexpected given that site selection was based on sediment geochemistry not revealing any petroleum hydrocarbons in these locations. This finding highlights the sensitivity and specificity of microbial DNA sequencing in environmental monitoring. Microbiome assessments like this one represent an important strategy for incorporating microbial biodiversity as a new and useful metric for establishing robust environmental baselines that are necessary for understanding ecosystem responses to marine pollution.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".