Bibliographic record
Abstract
Purpose: To determine the safety and effectiveness of three potential reagents which could be used to manage a simulated oil spill (SOS). Methods: The effectiveness of three agents used manage a simulated oil spill was evaluated: liquid soap, human hair, and Enviro-Bond 403 polymer. To evaluate safety, 1 hour-Artemia survival was evaluated; serial dilutions were employed to construct LC50 curves for a normal environment (control 1), for a SOS (control 2) and for a SOS managed with each of the 3 agents. Results: Hair and oil were effective absorbents as only 12% and 20% of the oil remained at 1 hour, repsectively; soap was ineffective with 62% of oil remaining unbound. Over a 1-hour period there was a 63% reduction in survival in Artemia exposed to an untreated SOS, when compared to natural conditions (p=0.001). Oil exposure exhibited a classic dose response curve as more Artemia died as its concentration increased; its LC50 was 17.5%. Hair and polymer were well tolerated by Artemia – neither reached their LC50 and approximately 80% of Artemia were alive at the end of one hour. Soap, with or without oil, was toxic to Artemia and its LC50 was 7%. These results were statistically significant between the three groups (ANOVA; p-value = <0.001). Conclusion: Hair and polymer we both effective and well tolerated by Artemia in a simulated oil spill. Objet: Pour déterminer la sécurité et l’efficacité de trois réactifs qui pourraient se servir à nettoyer une marée noire artificielle. Méthodes: L’efficacité de trois agents en nettoyant une marée noire artificielle a été évaluée: celle du savon liquide, de cheveux humains, et du polymère Enviro-Bond 403. Pour voir si ces agents sont sûrs, la survie de l’Artemia a été observée pendant une heure, et les dilutions en série ont été faites pour construire des courbes CL50 représentant un environnement normal (1er groupe témoin), une marée noire (2e groupe témoin), et une marée noire nettoyée avec chacun des trois agents. Résultats: Les cheveux et le polymère ont été des bons absorbants car seulement 12% et 20% du pétrole y restait après une heure, respectivement. Par contre, le savon a été inefficace car encore 62% du pétrole y restait. Pendant la première heure, la survie de l’Artemia dans une marée noire non traitée a été réduite par 63%, comparé aux conditions naturelles (p=0.001). L’exposition au pétrole a produit une courbe dose-réponse conventionnelle car plus d’Artemia sont morts quand la concentration du pétrole a été augmentée; son CL50 a été 17.5%. L’Artemia a supporté les cheveux et le polymère puisqu’aucun agent a atteint son CL50, et environ 80% de l’Artemia ont survécu après une heure. Le savon, n’importe s’il y avait du pétrole, a été toxique à l’Artemia et son CL50 a été 7%. Ces résultats ont été importants statistiquement parmi ces trois groupes (ANOVA; valeur p =<0.001). Conclusion: Les cheveux et le polymère ont été tous les deux des agents efficaces que l’Artemia a supporté dans un environnement de marée noire artificielle.
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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".