Killer whale fecal samples: How to get the most out of a single extraction
Bibliographic record
Abstract
Fecal samples are a non-invasive and relatively accessible matrix for investigating physiological processes in resident killer whale (Orcinus orca) populations. The high lipid content of the diet (primarily salmonids) leads to lower density fecal material and slower dispersion, facilitating sample collection. As fecal discharge is relatively infrequent and the volume of sample is variable, maximizing analytical options is an important consideration. Here we present an extraction methodology to measure hormones and lipid content from the same fecal aliquot. Lipid extractions are commonly conducted using chloroform and methanol from Folch or Bligh and Dyer (B&D), while alcohol is the primary solvent for hormone extraction. We evaluated the possibility of using the methanol layer from lipid extractions to assess fecal steroid hormone levels. Folch and B&D methanol residues were assayed form metabolites of progesterone (PMs) and corticosterone (GCs), and results were compared to aliquots extracted in 70 % ethanol. Hormone concentrations measured in the methanol layer from Folch and B&D extractions were 55 % to 79 % lower than concentrations in 70 % ethanol. We developed mathematical corrections, using linear regression models fitted to Folch or B&D methanol vs 70 % ethanol hormone concentrations (p < 0.01). Fecal concentrations of PMs and GCs from methanol extractions were biologically validated and are significantly higher in confirmed pregnant females compared to non-pregnant individuals (p < 0.05). This study demonstrates that lipid extraction protocols may be used for the analysis of multiple biomarkers, maximizing the use of small-volume samples.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.016 |
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".