Effects of temperature on instar number and larval development in the endangered longhorn beetle Callipogon relictus (Coleoptera: Cerambycidae) raised on an artificial diet
Bibliographic record
Abstract
Abstract Callipogon relictusSemenov (Coleoptera: Cerambycidae) is currently listed in the Red Data Books (Category I) of Russia and South Korea and, in 2006, was designated by the South Korean government as the first invertebrate priority target species in a restoration project. However, the species is also classified as an invasive quarantine pest by the Canadian Food Inspection Agency. Due to the five-year to seven-year life cycle of the species, experimental information about instar numbers has been poorly documented. Therefore, the goal of the present study was to document the instar numbers of non-diapauseCallipogon relictuslarvae reared on an artificial diet. Under conditions of 30 °C, 60% relative humidity, and constant dark (0:24 hour light-dark photoperiod), developmental pathways of 8, 10, and 12 instars were observed. The effect of temperature (20, 25, and 30 °C) on the duration of larval development was also examined to identify the optimum temperature for producingCallipogon relictusfor conservational purposes. Larvae reared at 30 °C and 60% relative humidity, without a chill period, developed in seven to eight months, which is about one-tenth the duration ofC. relictusdevelopment under natural conditions and the most rapid development ofC. relictusobserved to date.
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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".