Aquatic hazard assessment of a commercial sample of naphthenic acids
Bibliographic record
Abstract
This paper presents chemical composition and aquatic toxicity characteristics of a commercial sample of naphthenic acids (NAs). Naphthenic acids are derived from the refining of petroleum middle distillates and can contribute to refinery effluent toxicity. NAs are also present in oil sands process-affected water (OSPW), but differences in the NAs compositions from these sources precludes using a common aquatic toxicity dataset to represent the aquatic hazards of NAs from both origins. Our chemical characterization of a commercial sample of NAs showed it to contain in order of abundance, 1-ring>2-ring>acyclic>3-ring acids (∼84%). Also present were monoaromatic acids (7%) and non-acids (9%, polyaromatic hydrocarbons and sulfur heterocyclic compounds). While the acyclic acids were only the third most abundant group, the five most abundant individual compounds were identified as C(10-14) n-acids (n-decanoic acid to n-tetradecanoic acid). Aquatic toxicity testing of fish (Pimephales promelas), invertebrate (Daphnia magna), algae (Pseudokirchneriella subcapitata), and bacteria (Vibrio fischeri) showed P. promelas to be the most sensitive species with 96-h LL50=9.0 mg L(-1) (LC50=5.6 mg L(-1)). Acute EL50 values for the other species ranged 24-46 mg L(-1) (EC50 values ranged 20-30 mg L(-1)). Biomimetic extraction via solid-phase-microextraction (BE-SPME) suggested a nonpolar narcosis mode of toxic action for D. magna, P. subcapitata, and V. fischeri. The BE analysis under-predicted fish toxicity, which indicates that a specific mode of action, besides narcosis, may be a factor for fishes.
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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 | 0.001 | 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".