Simultaneous determination of steroid hormones and pharmaceuticals and personal care products with LC–MS/MS in feces of killer whales (Orcinus orca)
Bibliographic record
Abstract
A method enabling simultaneous measurement of steroid hormones and pharmaceuticals and personal care products (PPCPs) in fecal samples of killer whales (Orcinus orca) has been developed and validated with liquid chromatography–tandem mass spectrometry (LC–MS/MS). The compounds include a suite of hormones such as glucocorticoids, mineralocorticoid, androgens, estrogens, progestogens and PPCPs such as selective serotonin reuptake inhibitors and antibacterial and antifungal agents. This method can be used to assess killer whale reproduction, stress and other physiological responses as well as contaminant related exposure. Further, it provides a reference method for the validation of new immunoassays such as radio immunoassay and enzyme immunoassay for the detection of steroid hormones in this matrix. The optimized method involved an extraction of the freeze-dried fecal samples with reagent alcohol and water followed by purification of the extracts by solid phase extraction with hydrophilic-lipophilic balance (HLB) cartridge and liquid–liquid extraction with methyl tert-butyl ether (MTBE). Reconstituted extracts were analyzed by LC–MS/MS with an electrospray interface. This method has been successfully applied to the analysis of hormones and PPCPs in killer whale fecal material in support of research and monitoring initiatives that are aimed at assessing the health of both the Endangered Southern Resident Killer Whales (SRKW) and the Threatened Northern Resident Killer Whales. Results will support the Government of Canada’s Whales Initiative and its pursuit to assist in the recovery of SRKW.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".