Predicting the Relative Sensitivity of Sturgeons to Aryl Hydrocarbon Receptor Agonists
Bibliographic record
Abstract
Along with overexploitation and habitat loss, pollution is one cause for decreases in populations of fishes.One class of pollutants of particular global environmental concern to fishes are dioxinlike compounds (DLCs).DLCs elicit their toxicity through activation of the aryl hydrocarbon receptor (AHR).Despite this common mechanism of all DLCs, dramatic differences in sensitivity exist among fishes.Sturgeons (Acipenseridae) are an ancient family of fishes in which most species are endangered.It is hypothesized that pollutants, including DLCs, might be contributing to the observed declines in populations because sturgeons have a unique life-style that makes them susceptible to exposure to bioaccumulative chemicals.However, determining sensitivities of sturgeons to DLCs through traditional in vivo toxicity testing is not feasible for practical and ethical reasons.Therefore, the aim of this research was to develop a mechanismbased biological model capable of predicting the relative sensitivity of sturgeons to DLCs.This mechanism-based biological model was developed through investigations into the AHR and AHR-mediated molecular and biochemical responses of white sturgeon (Acipenser transmontanus) relative to teleost fishes and another species of sturgeon.White sturgeon responded to activation of the AHR in a manner that is consistent with responses of teleost fishes (induction of cytochrome P450 1A).Two AHRs with similar levels of expression were identified in white sturgeon, an AHR1 that resembles AHR1s of tetrapods and an AHR2 that resembles AHR2s of other fishes.Both AHR1 and AHR2 of white sturgeon were activated by exposure to five selected DLCs in vitro with effect concentrations less than any other AHR tested to date.These findings were suggestive that white sturgeon might be among the most sensitive species of fish to exposure to DLCs.These findings raised the question as to whether other members of the continuing to be a significant part of my time as a Ph.D. student and being a major influence on my growth as a scientist.Also special thanks to my Ph.D. supervisor Dr.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".