Can soil properties of Fluvisols be influenced by river flow gradient?
Notice bibliographique
Résumé
Received: 2018-04-19 | Accepted: 2018-05-15 | Available online: 2018-06-30 https://doi.org/10.15414/afz.2018.21.02.63-76 The occurrence of Fluvisols is associated with the rivers, which means that their properties can be greatly influenced by the fluvial activity of the rivers. Therefore, the aim of this work were to (1.) find out whether the flow gradient along the river influenced the soil properties of Fluvisols (2.) evaluate the soil properties of Fluvisols. Soil samples were taken from Nitra River Catchment between villages VýÄapy-Opatovce and JelÅ¡ovce near Nitra city. There were excavated five soil pits and soils were classified according to the World Reference Base for Soil Resources as follows: Profile 1 as Eutric Fluvisol (Loamic, Humic) (soil use: restored forest), Profile 2 as Eutric Fluvisol (Loamic, Humic) (soil use: arable soil), Profile 3 as Eutric Fluvisol (Loamic, Humic) (soil use: fallow soil), Profile 4 as Eutric Gleyic Fluvisol (Loamic, Humic) (soil use as: forest), Profile 5 as Eutric Fluvisol (Loamic, Humic) (soil use: r aid forest). The investigated Fluvisols had different chemical and physical properties, but not as a consequence of the flow gradient along the river. Differences in chemistry and physical properties of Fluvisols developed along the Nitra River have been significantly affected mainly by its use, soil management practices and depth of the soil profile. Keywords: physical and hydrophysical properties, soil structure, soil sorptive parameters, Fluvisols References ATSIVOR, L. et al. 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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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,006 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».