Can the Identification of Odorants Within a Mixture Be Trained?
Bibliographic record
Abstract
Identifying odors within mixtures is a difficult task: humans are able to recognize only up to 4 odors within a mixture. We wanted to test the effects of olfactory training on this ability. We used 7 odorants to create 35 olfactory stimuli of 1, 2, 3, 4, or 5 odorants. The task consisted of identifying the odorants present within the mixture. We trained novices on this task for 5 days: they came to the laboratory to perform the task once a day before coming back for the final testing. Then, we compared them to sommeliers, thus olfaction experts, and untrained novices. Results showed that sommeliers outperformed the other groups with mixtures of up to 4 odorants but not with mixtures of 5 odorants. The short olfactory training allowed trained participants to perform as well as sommeliers when it came to identifying single odorants but was not enough to improve their performance when stimuli were mixtures of 2 or more odorants. This study supports the idea that the number of odors we can recognize within a mixture is limited but suggests training can improve the performance: a short olfactory training is enough to enhance the ability to identify single odorants, whereas expertise refines identification ability of mixtures of up to 4 odorants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".