Enhancing the usefulness of artificial seeds in seed beetle model systems research
Bibliographic record
Abstract
Abstract Seed beetles are among textbook examples of experimental model systems used to better understand nature's complexities. A potential seed beetle model systems' strength is the use of artificial seeds to remove experimental confounds. This is particularly relevant for scaling life histories to population dynamics but requires many artificial seeds. Current methods of producing seeds are laborious, limiting their application. Building on previous work, we developed efficient methods to produce artificial seeds and expand their use. We outline steps to produce artificial seeds and describe a new technique for transferring beetle eggs laid on natural seeds to artificial seeds. Our methods yielded a 100‐fold increase in artificial seed production that is 80% more efficient than current methods. Burrowing success of beetle larvae from eggs laid on natural seeds and transferred to artificial seeds (85.4%) was comparable to rates on natural seeds. Streamlining artificial seed production enables highly replicated life‐history and time‐series assays with large sample sizes. The ability to transfer eggs from natural to artificial seeds allows research on many phenomena including oviposition strategies, maternal provisioning and competitive strategies, broadening the usefulness of seed beetle model systems in ecological and evolutionary research.
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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 | 0.002 | 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) | 0.000 | 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".