Crude Oil Metagenomics for Better Bioremediation of Contaminated Environments
Bibliographic record
Abstract
Our planet suffers more and more from various pollution problems. The marine environments are always regarded as sewers without end and are then subjected to different types of toxic rich rejects leading to oceans and seas degradation. The marine environment becomes at the same time the witness and the actor of the history of the planet, and its chemical composition witnesses all the complexity of its evolution. Coastal regions are often where we find various pollutants. Seawater (Jaffrennou et al. 2007; Perez-Carrera et al. 2007), marine sediments (Wakeham, 1996), and interstitial water (Perez-Carrera et al., 2007) have shown this pollution. Petroleum hydrocarbons are among the most toxic compounds poured at sea. Known as the most significant pollutant, crude oil can persist for years (Burns & Teal 1979), with dangerous effects on coastal environments and negative effects on both the ecosystem and the marine biodiversity (Clark 1992; Rice et al., 1996). In marine environment, crude oil is subjected to physico-chemical and biological modifications, which enhance hydrocarbons solubility in the water and consequentially cause extensive damage to marine life, natural resources, and human health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".