In Situ Experimental Assessment of Lake Whitefish Development Following a Freshwater Oil Spill
Bibliographic record
Abstract
Wabamun Lake (Alberta, Canada) has been subject to ongoing contamination with polycyclic aromatic hydrocarbons (PAHs) from multiple sources for decades and in August 2005 was exposed to ca. 149 500 L of bunker C oil following a train derailment. We compared the pattern, frequency, and severity of deformity in larvae of lake whitefish ( Coregonus clupeaformis ) incubated in situ in areas of Wabamun Lake exposed only to “background” PAH contamination and in areas additionally exposed to PAHs from the oil. All sites in the lake (including reference areas) showed incidences of deformity higher than are typically observed in laboratory studies. A small number of oil-exposed sites showed higher incidences of some teratogenic deformities and a tendency to exhibit deformities of higher severity than sites not exposed to oil. The frequency of moderate to severe deformities in 8 of 16 classes was correlated with PAH exposure. Nonmetric multivariate ordination of deformity data revealed a general pattern of increasing incidence and severity of several skeletal (lordosis, scoliosis) and craniofacial (ocular, jaw) deformities at sites with relatively high exposure to oil-derived PAHs. A simultaneous consideration of incidence, severity, and pattern of deformity enabled us to detect a consistent (overall ∼5% above background) response to the oil despite high variability and high background deformity rates in this historically contaminated environment.
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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 | 0.001 | 0.001 |
| 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".