Challenges with effective nutrient supplementation for amphibians: A review of cricket studies
Bibliographic record
Abstract
Over the last 25 years, numerous studies have investigated the impact of insect supplementation on insect nutrient content. In light of recent nutrition related challenges with regards to zoo amphibians fed an insect based diet, this review attempts to comprehensively compile both anecdotal and published data in the context of practical application on this topic. Insects, primarily crickets, used for amphibian diets historically demonstrate low concentrations of key nutrients including calcium and vitamin A. Commonly used practices for supplementation involving powder dusting or gut loading have been shown to improve delivery of calcium and vitamin A, though often not reaching desired nutrient concentrations. The large variety of factors influencing insect nutrient content are difficult to control, making study design, and results often inconsistent. Formulation and availability of more effective gut loading diets, combined with a standardized protocol for insect husbandry and dietary management may be the most effective way to supplement insects for use in amphibian feeding programs. Ideally, the nutritional improvement of feeder insects would begin at the breeder level; however, until this becomes a viable choice, we confirm that supplementation of crickets through both gut-loading and dusting appear necessary to support the nutritional health of amphibians and other insectivores in managed collections.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".