mRNA analysis in flattened fauna: obtaining gene-sequence information from road-kill and game-hunting samples
Bibliographic record
Abstract
Whether gene-sequence information could be obtained using mRNA from road-kill and hunting samples was investigated. Adipose tissue was used to clone cDNA fragments of the hormone leptin and brain tissue was used for the enzyme glutamic acid decarboxylase (GAD). Tissues collected from road-killed animals were used to clone leptin from RNA samples of raccoon (Procyon lotor) and woodchuck (Marmota monax). We were able to extract RNA and clone GAD67 from samples of masked shrew (Sorex cinereus), although the time of death was unknown. We collaborated with hunters who provided tissues from which we cloned leptin and GAD isoforms from beaver (Castor canadensis), red squirrel (Tamiasciurus hudsonicus), black bear (Ursus americanus), and moose (Alces alces americana). Molecular phylogenetic analyses confirmed that the sequences obtained did not result from contamination. A time-course experiment showed that even 24 h after the death of rats, sufficient mRNA remains to amplify leptin from adipose tissue. These results suggest that road-kill and hunting samples could be used as a valuable source of gene-sequence information.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".