TOXICITY AND TOXICOKINETICS OF BINARY COMBINATIONS OF PETROLEUM HYDROCARBON DISTILLATES WITH THE EARTHWORM <i>EISENIA ANDREI</i>
Bibliographic record
Abstract
Petroleum hydrocarbons (PHCs) act via narcosis and are expected to have additive toxicity. However, previous work has demonstrated less-than-additive toxicity with PHC distillates and earthworms. A study was initiated to investigate this through toxicity and toxicokinetic studies with the earthworm Eisenia andrei. Three petroleum distillate fractions, F2 (>C10-C16), F3a (>C16-C23), and F3b (>C23-C34), were used in two binary combinations, F2F3a and F3aF3b. In the toxicity study, clean soil was spiked with equitoxic combinations of the two distillates ranging from 0.5 to 2.5 toxic units. In the toxicokinetic study, a binary combination consisting of one concentration of each distillate was used. On a soil concentration basis, the toxicity of the binary combinations of distillates was less than additive. Accumulation of the individual distillates, however, was generally reduced when a second distillate was present, resulting in lower body burden. This is thought to be due to the presence of a nonaqueous-phase liquid at the soil concentrations used. On a tissue concentration basis, toxicity was closer to additive. The results demonstrate that tissue concentrations are the preferred metric for toxicity for earthworms. They also demonstrate that the Canada-wide soil standards based on individual distillates are likely protective.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".