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Record W2171869842 · doi:10.1897/07-139.1

Bone-related effects of contaminants in seals may be associated with vitamin D and thyroid hormones

2008· article· en· W2171869842 on OpenAlexaboutno aff
Heli Routti, Madeleine Nyman, Bjørn Munro Jenssen, Christina Bäckman, Jaana Koistinen, Geir Wing Gabrielsen

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhocaThyroidHormoneVitamin D and neurologyEndocrinologyHarbor sealInternal medicineResorptionPhysiologyBiologyMedicineZoology

Abstract

fetched live from OpenAlex

The high levels of polychlorinated biphenyls (PCBs) and DDT in gray seal (Halichoerus grypus) and ringed seal (Phoca hispida botnica) in the Baltic Sea have been associated with pathological disruptions, including bone lesions and reproductive failures. The underlying environmental and toxicological mechanisms leading to these pathological changes are not yet fully understood. The present study investigated the relationship between the individual contaminant load and bone- and thyroid-related effects in adult gray seals (n=30) and ringed seals (n=46) in the highly contaminated Baltic Sea and in reference areas (Sable Island, Canada, and Svalbard, Norway). In the gray seals, multivariate and correlation analyses revealed a clear relationship between circulating 1,25-dihydroxyvitamin D(3) (1,25(OH)(2)D), calcium, phosphate, and thyroid hormone (TH) levels and hepatic PCB and DDT load, which suggests contaminant-mediated disruption of the bone and thyroid homeostasis. Contaminants may depress 1,25(OH)(2)D levels or lead to hyperthyroidism, which may cause bone resorption. In the ringed seals, associations between circulating 1,25(OH)(2)D, THs, and hepatic contaminants were less prominent. These results suggest that bone lesions observed in the Baltic gray seals may be associated with contaminant-mediated vitamin D and thyroid disruption.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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.

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

Citations67
Published2008
Admission routes1
Has abstractyes

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