What an odour descriptor corpus can and cannot measure: valence, attenuation, and the ceiling of the public record

Abstract:Machine olfaction trains on pooled public descriptor corpora, but whether a shared descriptor word measures the same thing across corpora has not been tested, nor has the ceiling of what any of them can measure. We audit four corpora from Pyrfume. Conditioning on the molecule makes McNemar's test the exact conditional test of the corpus effect. Corpora disagree heterogeneously across descriptors ($I^2 = 80\%$) and non-uniformly with labelling breadth ($z = 17.2$), so no single offset repairs pooling. Median tetrachoric agreement is 0.795 against median $\kappa$ of 0.413: sources largely concur on which molecules deserve a word and differ on how readily they apply it. Of 109 descriptors with an estimable effect, 36 show large differential functioning on the ETS scale. Against a human panel's reliability, Morgan fingerprints with the full RDKit descriptor block reach 32.9\% of achievable; adding every label from two merged corpora reaches 33.9\%. The gap does not close with model capacity, encoding choice, more molecules, or more words. The missing variance is valence. One pleasantness rating per molecule reaches 54.6\% of achievable (57.1\% on an independent older instrument). Valence recovered from descriptors ($\rho = 0.457$) yields only 16.6\%, so it must be measured. Five raters exceed structure plus the full descriptor record; fifteen to twenty saturate. We release a descriptor crosswalk and twenty machine-checked theorems.

Machine Learning (cs.LG)

arXiv:2609.12875 [cs.LG]

(or arXiv:2609.12875v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12875 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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