Best available technique for the recovery of marine benthic communities in a gravel shore after the oil spill: A mesocosm-based sediment triad assessment
Bibliographic record
Abstract
Ecotoxicological effects of spilled oils are well documented, but study of recovery of marine benthic communities is limited. Long-term recovery of hard bottom communities during physical and biological remediations after a spill was monitored. A 60-day experiment was conducted using a mesocosm with monitoring of eight endpoints by use of the sediment quality triad (SQT). First, physical treatment of hot water + high pressure flushing maximally removed residual oils (max=93%), showing the greatest recovery among SQT variables (mean=72%). Physical cleanup generally involved adverse effects such as depression of the microphytobenthic community during the initial period. Next, biological treatments, such as fertilizer, emulsifier, enzyme and augmentation of the microbes, all facilitated removal of oil (max=66%) enhancing ecological recovery. Analysis of the microbiome confirmed that oil-degrading bacteria, such as Dietzia sp. and Rosevarius sp. were present. A mixed bioremediation, including fertilizer + multi-enzyme + microbes (FMeM) maximized efficacy of remediation as indicated by SQT parameters (mean=47%). Natural attenuation with "no treatment" showed comparable recovery to other remediations. Considering economic availability, environmental performance, and technical applicability, of currently available techniques, combined treatments of physical removal via hand wiping followed by FMeM could be most effective for recovery of the rocky shore benthic community.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".