Monitoring sea lamprey pheromones and their degradation using rapid stream‐side extraction coupled with <scp>UPLC</scp>‐<scp>MS</scp>/<scp>MS</scp>
Bibliographic record
Abstract
Pheromones guide adult sea lamprey (Petromyzon marinus) to suitable spawning streams and mates, and therefore, when quantified, can be used to assess population size and guide management. Here, we present an efficient sample preparation method where 100 mL of river water was spiked with deuterated pheromone as an internal standard and underwent rapid field-based SPE and elution in the field. The combination of field extraction with laboratory UPLC-MS/MS reduced the sample consumption from 1 to 0.1 L, decreased the sample process time from more than 1 h to 10 min, and increased the precision and accuracy. The sensitivity was improved more than one order of magnitude compared with the previous method. The influences of experimental conditions were assessed to optimize the separation and peak shapes. The analytical method has been validated by studies of stability, selectivity, precision, and linearity and by the determination of the limits of detection and quantification. The method was used to quantify pheromone concentration from five streams tributary to Lake Ontario and to estimate that the environmental half-life of 3kPZS is about 26 h.
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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 | 0.001 | 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".