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Exploring Spatial and Temporal Variations of Cadmium Concentrations in Pacific Oysters from British Columbia

2010· article· en· W1972693523 on OpenAlexafffundabout
Cindy Feng, Jiguo Cao, Leah Bendell

Bibliographic record

VenueBiometrics · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsOysterCadmiumPacific oysterCrassostreaBayFisheryEnvironmental scienceShellfishAquacultureOceanographyBiologyFish <Actinopterygii>GeologyAquatic animalChemistry

Abstract

fetched live from OpenAlex

Oysters from the Pacific Northwest coast of British Columbia, Canada, contain high levels of cadmium, in some cases exceeding some international food safety guidelines. A primary goal of this article is the investigation of the spatial and temporal variation in cadmium concentrations for oysters sampled from coastal British Columbia. Such information is important so that recommendations can be made as to where and when oysters can be cultured such that accumulation of cadmium within these oysters is minimized. Some modern statistical methods are applied to achieve this goal, including monotone spline smoothing, functional principal component analysis, and semi-parametric additive modeling. Oyster growth rates are estimated as the first derivatives of the monotone smoothing growth curves. Some important patterns in cadmium accumulation by oysters are observed. For example, most inland regions tend to have a higher level of cadmium concentration than most coastal regions, so more caution needs to be taken for shellfish aquaculture practices occurring in the inland regions. The semi-parametric additive modeling shows that oyster cadmium concentration decreases with oyster length, and oysters sampled at 7 m have higher average cadmium concentration than those sampled at 1 m.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.221
Teacher spread0.182 · 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 teacher head, not a consensus.

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

Citations8
Published2010
Admission routes3
Has abstractyes

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