Maternally transmitted isotopes and their effects on larval fish: a validation of dual isotopic marks within a meta-analysis context
Bibliographic record
Abstract
Transgenerational marking enables mass-marking of larval fishes via transmission of enriched stable isotopes from mother to offspring, but potential impacts on the resultant progeny are poorly understood. We injected enriched stable isotopes (137Ba and87Sr) into female purple-spotted gudgeon, Mogurnda adspersa, to produce multiple batch markers and examined larval morphology at hatch as well as survival and growth to 31 days posthatch in marked and unmarked offspring. Transgenerational marking had minimal effects on larval growth and survival, whereas body depth at hatch was significantly reduced in marked larvae. A meta-analysis of transgenerational marking effects on larval morphology at hatch and growth rates across multiple fish species found a nonsignificant positive effect of enriched stable barium isotopes on larval morphology at hatch, but a significant negative effect on growth. There were no significant effects of strontium on morphology or growth. Meta-regression analysis revealed that larval size at hatch increased with the dose of injected stable barium isotopes, but this result should be interpreted cautiously. Because of high levels of between-study heterogeneity, we caution against assuming there are no effects of transgenerational marking on fish offspring; any such effects should be validated and incorporated into transgenerational marking studies of fish dispersal.
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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 | 0.040 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.020 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".