Developmental and Environmental Stability of Candidate Reference Genes in the Wild Bee <i>Ceratina calcarata</i>
Bibliographic record
Abstract
Abstract Quantitative real-time PCR (RT-qPCR) is a widely used method for measuring gene expression, but its accuracy depends on the use of stable reference genes for data normalization. In this study, we evaluated the expression stability of seven candidate reference genes ( RPS18, RPS5, RPL32, RPL8, EF-1 α, β -Actin , and GAPDH ) in the small carpenter bee Ceratina calcarata across developmental stages (larvae, pupae, adults) and different landscape environments (conventional farms, organic farms, and roadside sites). Using four analytical algorithms, GeNorm, NormFinder, BestKeeper, and the comparative ΔCt method, we identified RPS18 and RPL8 as the most stable reference genes under varying biological and environmental conditions. These findings were further supported by RefFinder, which integrates results from all algorithms. Our study provides the first validated reference genes for C. calcarata , enabling more accurate and reproducible gene expression analysis in this ecologically important wild bee species. This work will support future research in pollinator biology, environmental stress responses, and conservation genomics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".