Supporting data for the manuscript entitled: Long-term biodegradation of oil under Arctic Conditions: the Baffin Island Oil Spill (BIOS) revisited after almost four decades.
Bibliographic record
Abstract
The potential of an unintentional oil spill from ships or upcoming oil development is rising as the Arctic warms up more. It is crucial to understand how oil behaves in this setting and what influences oil biodegradation in the Arctic. On Baffin Island in the Canadian High Arctic, the Baffin Island Oil Spill (BIOS) project staged a number of simulated oil spills on the backshore zone of beaches in the 1980s. Two BIOS sites were revisited in 2019, about 40 years after the first oil pollution, providing a unique chance to research the long-term weathering of crude oil in Arctic conditions. Here, we demonstrate that even after over 40 years, residual oil is still detectable at these locations. Oil at both locations seems to deteriorate relatively slowly, with estimated loss rates ranging from 1.8 to 2.7% annually and appearing to vary across the two locations. We also demonstrate how the locations' sediment microbial communities are still severely impacted by leftover oil, as seen by a decline in diversity, variations in microbial load, and an enrichment of reported oil-degrading bacteria in contaminated sediments. The most enriched putative oil degraders were found in non-oiled control sediments as well, indicating that these degraders are a normal component of the Arctic sediment microbiome even in the absence of oil. Reconstructed genomes of putative oil degraders indicate that only a portion of these degraders have unique adaptations for growth in psychrothermic environments. This study's findings taken together demonstrate that oil spills in the Arctic can linger and have a lasting, major impact on the environment for decades.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.802 | 0.377 |
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".