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Enregistrement W4280552350 · doi:10.1255/tosf.171

Estimating the heterogeneity invariant using size-density classes – the case of contaminated soil and complex materials

2022· article· en· W4280552350 sur OpenAlexaff
Jean‐Sébastien Dubé, Kim H. Esbensen

Notice bibliographique

RevueTOS forum · 2022
Typearticle
Langueen
DomaineComputer Science
ThématiqueGeochemistry and Geologic Mapping
Établissements canadiensÉcole de Technologie Supérieure
Organismes subventionnairesnon disponible
Mots-clésSampling (signal processing)Variance (accounting)Sampling theoryStatisticsVariance reductionMathematicsField (mathematics)Sample size determinationEconometricsComputer scienceStatistical physicsPhysicsMonte Carlo methodPure mathematics

Résumé

récupéré en direct d'OpenAlex

For the important material class of aggregate mixtures comprised by both analyte-enriched and analyte-coated particles, Gy’s classical s2(FSE) formula has often been reported to yield estimates of the fundamental sampling variance greater than the empirically estimated sampling variance. Which, however, is physically impossible according to the tenets of the Theory of Sampling (TOS), both physically and logically, since the fundamental sampling variance is, by definition, the minimum sampling variance remaining after all other sources of sampling errors have been eliminated. This situation has for many decades hindered rational use of the Theory of Sampling for this kind of complex systems. We here focus on contaminated soil as a typical illustrative example of great interest, as well as more generally in the field of environmental site assessment. This uncomfortable situation is exacerbated by the fact that sampling in these fields is still, after 70+ years of TOS, largely conducted by grab sampling, which assuredly lead to significant uncertainty and bias. However, there is a solution to this at first sight“intractable” problem to be found, specifically within TOS. In some of his earlier publications, Gy developed a variant the s2(FSE) formula based on consideration of both size- and density classes, but quickly dismissed this approach as being inapplicable to « the metal, mining, and processing industries […] due to the unusual density contrast between the components » typical of matrices sampled in these fields. This size-density class variant was consequently then left out of sampling awareness and literature for a long time. We revisit herein development of the heterogeneity invariant on this basis and show this to represent a general option which can be adapted to distinct and specific matrices andanalytes beyond the original restricted realm. As an example, a size-density class variant is applied to data from studies on sampling complex contaminated soils for which the use of Gy’s classical formula yielded such “impossible” estimates of s2(FSE) larger than empirical sampling variances by several orders of magnitude. This size-density class s2(FSE) variant now provides estimates for all cases and examples, which are systematically smaller than the empirical sampling variances, and thus in full accordance with the Theory of Sampling. This generalised approach is also applied with similar success to controlled materials, which were made to represent analyte-enriched and/or analyte-coated matrices, as used in recent studies on sampling bias and representativeness. The results in our studies all show that it is not Gy’s classical formula which was at fault when applied outside the traditional domains, e.g., to contaminated soils, it is that the critical assumptions behind the formula were broken, unwittingly, or worse, with blunt carelessness. In analyte-coated materials, or for mixed matrices, the original full size-density class-based formula now provides the proper starting point for developing TOS-compliant matrix-specific approaches on a much broader scale. With this new scope, analysts no longer must forego the revolutionary advantage of Gy’s classical formula, i.e., the capacity to estimate the fundamental sampling variancea priori. Now, only at the cost of a pilot sampling stage, the augmented size-density class formula provides the analyst with the capacity to adapt sampling protocols also to the challenging task of taking on practically all natural systems sesu lato, however complex.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,768
Score d'incertitude au seuil0,699

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,258
Écart entre enseignants0,223 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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