Terrestrial Toxicity Testing with Volatile Substances
Bibliographic record
Abstract
The toxicity of volatile hydrocarbons to terrestrial organisms is currently being investigated. It was found that toxicity test methods required procedural modification during test soil preparation to minimize the loss of the test substance due to volatilization. A series of tests was initiated with an artificial and field-collected reference soil to investigate alternative test-soil, preparation, methodologies for evaluating motor gasoline (mogas) as it predominantly contains highly volatile low-end carbon components (⩽C5–C13). The proposed approaches to minimize volatilization losses included the application of mogas at concentrations sufficiently high to accommodate the percentage lost during test soil preparation; the modification of test soil preparation methods; and the modification of the nature of test units. The analyses of benzene, toluene, ethylbenzene, xylene (BTEX) compounds, total purgeable hydrocarbons (TPuH), total extractable hydrocarbons (TEH), and total petroleum hydrocarbons (TPH) from soil samples quantified the losses of these mogas constituents at each stage of preparation. The constituents of the mogas were found to volatilize at different rates and the losses were to some extent, concentration dependent. Amending test soils at higher concentrations than required for an adverse effect compensated for the rate of the losses due to volatilization. Significant amounts of the mogas were lost regardless of the methods used to prepare the test soils. Alternative “closed” (minimized air exchange) test units were considered useful and acceptable, as they optimized exposure of the test organism to mogas and did not compromise their survival. The test methods developed to assess the toxicity of mogas in terrestrial organisms may be equally applicable to the toxicity testing of other highly volatile substances.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".