Hormonal induction of spawning in 4 species of frogs by coinjection with a gonadotropin-releasing hormone agonist and a dopamine antagonist
Bibliographic record
Abstract
BACKGROUND: It is well known that many anurans do not reproduce easily in captivity. Some methods are based on administration of mammalian hormones such as human chorionic gonadotropin, which are not effective in many frogs. There is a need for simple, cost-effective alternative techniques to induce spawning. METHODS: Our new method is based on the injection of a combination of a gonadotropin-releasing hormone (GnRH) agonist and a dopamine antagonist. We have named this formulation AMPHIPLEX, which is derived from the combination of the words amphibian and amplexus. This name refers to the specific reproductive behavior of frogs when the male mounts and clasps the female to induce ovulation and to fertilize the eggs as they are laid. RESULTS: We describe the use of the method and demonstrate its applicability for captive breeding in 3 different anuran families. We tested several combinations of GnRH agonists with dopamine antagonists using Lithobates pipiens. The combination of des-Gly10, D-Ala6, Pro-LHRH (0.4 microrams/g body weight) and metoclopramide (10 micrograms/g BWt. MET) was most effective. It was used in-season, after short-term captivity and in frogs artificially hibernated under laboratory conditions. The AMPHIPLEX method was also effective in 3 Argentinian frogs, Ceratophrys ornata, Ceratophrys cranwelli and Odontophrynus americanus. CONCLUSION: Our approach offers some advantages over other hormonally-based techniques. Both sexes are injected only once and at the same time, reducing handling stress. AMPHIPLEX is a new reproductive management tool for captive breeding in Anura.
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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 | 0.001 | 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) | 0.002 | 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".