Data from: Gene transcription in sea otters (Enhydra lutris): development of a diagnostic tool for sea otter and ecosystem health
Bibliographic record
Abstract
Gene transcription analysis for diagnosing or monitoring wildlife health requires the ability to distinguish pathophysiological change from natural variation. Herein we describe methodology for the development of quantitative real time polymerase chain reaction (qPCR) assays to measure differential transcript levels of multiple immune-function genes in the sea otter (Enhydra lutris); sea otter specific, qPCR primer sequences for the genes of interest are defined. We establish a “reference” range of transcripts for each gene in a group of clinically healthy captive and free-ranging sea otters. The 10 genes of interest represent multiple physiological systems that play a role in immuno-modulation, inflammation, cell protection, tumor suppression, cellular stress-response, xenobiotic metabolizing enzymes, antioxidant enzymes, and cell-cell adhesion. The cycle threshold (CT) measures for most genes were normally distributed; the complement cytolysis inhibitor was the exception. The relative enumeration of multiple gene transcripts in simple peripheral blood samples expands the diagnostic capability currently available to assess the health of sea otters in situ and provides a better understanding of the state of their environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".