Comment on egusphere-2023-1970
Notice bibliographique
Résumé
Abstract. In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last two decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the so-called three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, contribution modelling tendency and performance on a single dataset. As for the tree-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracing properties analysed in soil (through the consideration of three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated in order to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was used to predict source contributions on virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their more or less restrictive identification of conservative properties, which were found to be associated with different sediment source contribution tendencies. The less restrictive selections of tracers were associated with a dominant and constant contribution of forests to sediment, whereas the most restrictive selections were associated with dominant and constant contributions of cropland to sediment. In contrast, intermediately restrictive selection of tracers led to more balanced contributions of both cropland and forest to sediment production. Virtual mixtures allowed to compute several evaluation metrics, which supported a better understanding of each tracer selection modelling accuracy. However, strong divergences were observed between the predicted contributions of virtual mixtures and the predicted sediment source contributions. These divergences may likely be attributed to the occurrence of a non-(fully) conservative behaviour of potential tracing properties during erosion, transport and deposition processes, which could not be reproduced when generated the virtual mixtures. Among the compared tracer selection methods, the three-step method using the mean ± SD and hinge range test criteria provided the most reliable tracer selection methods. In the future, it would be fundamental to generate more reliable metrics to assess conservativeness, to support more reliable modelling and more realistic virtual mixture generation to correctly evaluate modelling accuracy. These improvements may contribute to trustworthy sediment fingerprinting techniques for supporting efficient soil conservation and watershed management.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,023 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,183 | 0,168 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».