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Record W3084021919 · doi:10.26577/ijbch.2020.v13.i1.03

Histological structure of thyroid gland and level of thyroid hormones in tadpoles exposed to oil and petroleum products

2020· article· en· W3084021919 on OpenAlexaff
L. Sutuyeva, Tamara Shalakhmetova, Vance L. Trudeau

Bibliographic record

VenueInternational Journal of Biology and Chemistry · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThyroidThyroid functionBioindicatorHormoneAmphibianRana ridibundaBiologyEnvironmental chemistryInternal medicineEndocrinologyEcologyChemistryMedicine

Abstract

fetched live from OpenAlex

Growing global demand and growth in oil production and refining lead to an increase in environmental pollution by waste from these industries. Huge territories of Kazakhstan are influenced by the activities of the oil and oil refining industries. The consequence of this is the deterioration of ecosystems in the oil-producing regions, the decline in biodiversity and deterioration of public health. In this regard, there is a need in an informative bioindicator for studying the state of ecosystems of oil producing regions. The aim of this research was to evaluate the effects of oil and petroleum products exposure on the function of thyroid gland in tadpoles of local amphibian species. The study revealed that chronic exposure to watersoluble fraction of oil, o-xylene or diesel fuel causes hypertrophy and hyperplasia of the thyroid follicular cells, a decrease in the colloid volume in the follicles, as well as a decrease in the content of thyroid hormones in the tadpoles of the marsh frog (Rana ridibunda) and green toad (Bufo viridis), which indicates a suppression of thyroid function.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 designBench or experimental
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

Citations2
Published2020
Admission routes1
Has abstractyes

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