A standardized lexicon of body odor words crafted from 17 countries
Bibliographic record
Abstract
Body odors offer a unique window into the physiological and psychological profile of the emitter. This information, broadcast in nonverbal communication, significantly shapes social interactions. However, effectively digitizing body odors requires a precise framework for perceptual operationalization. Previous research has used a very limited number of verbal terms, such as pleasant, intense, or attractive, which fails to adequately capture qualitative differences. To address this gap, we elicited body odor descriptions from 2,607 participants across 17 countries and 13 languages. All these descriptions are presented here in one dataset, together with a condensed list of 25 body odor words (BOW). Those terms reliably differentiated between body states, and were validated in a separate study with a different group of 155 perceivers. The dataset, available as a web application, provides a novel operationalization of body odor impressions, which is a precondition for studying olfaction in human nonverbal communication, for perception-based digitization of body odors and for comparative studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".