The “<scp>S</scp>ecret” in Secretions: Methodological Considerations in Deciphering Primate Olfactory Communication
Bibliographic record
Abstract
Olfactory communication in primates is gaining recognition; however, studies on the production and perception of primate scent signals are still scant. In general, there are five tasks to be accomplished when deciphering the chemical signals contained in excretions and secretions: (1) obtaining the appropriate samples; (2) extracting the target organic compounds from the biological matrix; (3) separating the extracted compounds from one another (by gas chromatography, GC or liquid chromatography, LC); (4) identifying the compounds (by mass spectrometry, MS and associated procedures); and (5) revealing biologically meaningful patterns in the data. Ultimately, because some of the compounds identified in odorants may not be relevant, associated steps in understanding signal function involve verifying the perception or biological activity of putative semiochemicals via (6) behavioral bioassays or (7) receptor response studies. This review will focus on the chemical analyses and behavioral bioassays of volatile, primate scent signals. Throughout, we highlight the potential pitfalls of working with highly complex, chemical matrices and suggest ways for minimizing problems. A recurring theme in this review is that multiple approaches and instrumentation are required to characterize the full range of information contained in the complex mixtures that typify primate or, indeed, many vertebrate olfactory cues. Only by integrating studies of signal production with those verifying signal perception will we better understand the function of olfactory communication.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".