Exploring stress biomarkers in an avian model
Bibliographic record
Abstract
Biomarkers are measurable biological molecules that can be used as indicators of stress in animals. Presently, there is little understanding of stress biomarkers in birds. The objective of this study is to identify changes in unique proteins in an avian model to further our understanding of the stress response in Aves. The models chosen in this study were the Herring Gull (Larus argentatus) and Great Black-Backed Gull (L. marinus). Specimens were obtained from the St. John's International Airport, Newfoundland and Labrador, Canada. Samples of liver tissue were ground in a mortar and pestle under sterile conditions to extract soluble proteins. Protein concentrations were normalized and 1D SDS-PAGE followed by a modified silver staining method were used to identify individual protein profiles. Gels were imaged and digitized using a Fluor-STM Multi-imager. Protein spots were excised from the gels and further analyzed using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Preliminary MS data indicates the presence of known stress proteins, including heat shock-70, heat shock-90, anti-oxidants such as superoxide dismutase and, pyridoxine phosphate oxidase (an essential enzyme in vitamin B6 metabolism). These proteins will be quantified by immunoblotting and used as targets to identify the effects of environmental stressors on the gulls. As our knowledge of stress in Aves is limited, these data will contribute to the broader understanding of stress in Aves.
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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".