Dose-dependent uptake, elimination, and toxicity of monosodium methanearsonate in adult zebra finches (<i>Taeniopygia guttata</i>)
Bibliographic record
Abstract
Monosodium methanearsonate (MSMA), an arsenic-based pesticide, has been used for the past 10 years in attempts to suppress mountain pine beetle (Dendroctonus ponderosae) outbreaks in British Columbia, Canada. Previous studies have shown that cavity nesting forest birds such as woodpeckers forage and breed in MSMA treated pine stands. Here we examined the effects of MSMA in the laboratory using the zebra finch (Taeniopygia guttata), with the objective to examine tissue distribution and sublethal toxic effects in a model avian species. Zebra finches were exposed to this pesticide at doses similar to those found in bark beetle samples from MSMA stands of trees treated in the southern interior of British Columbia (8, 24, and 72 microg/g/d and a control group). Results showed high excretion (>90%) of arsenic in all dose groups, as well as dose-dependent trends in accumulation of arsenic in the blood (p < 0.001) and specific tissues. Monomethylarsonic acid, MMA (V), was the predominant form of arsenic in the blood plasma. Dimethylarsinic acid was the major form of arsenic found in the liver (83%) and kidney (61%) tissues. The brain tissue contained primarily the MMA (V) form (57%). Significant weight loss occurred in the two highest dose groups (p < 0.05). Birds in the highest dose group lost up to 15% of initial body mass.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".