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Record W1995376910

Environmental costs of shipping.

2007· article· en· W1995376910 on OpenAlexaffabout
Adrian M.H. deBruyn, Barbara Gail Wernick, Corey Stefura, Blair McDonald, Barri-Lynn Rudolph, Luanne Patterson, Peter M. Chapman

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCoregonus clupeaformisDeformityContaminationToxicologyIncidence (geometry)Oil spillEnvironmental scienceBiologyMedicineEcologySurgeryFisheryEnvironmental engineeringFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

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 approximately 5% above background) response to the oil despite high variability and high background deformity rates in this historically contaminated environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.159
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1570.031

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.

Opus teacher head0.011
GPT teacher head0.197
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2007
Admission routes2
Has abstractyes

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