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Enregistrement W4405061868 · doi:10.31861/geo.2024.847.155-168

Territorial and temporal features of the land structure of the physical and geographical districts of the Chernivtsi region

2024· article· en· W4405061868 sur OpenAlexaboutno aff
Аліна Дячук, Halyna Kovbinka, Vitalii Prysakar, Iryna Dobynda

Notice bibliographique

RevueScientific Herald of Chernivtsi University Geography · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueScientific Research and Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGeographyLand useAgricultural landWork (physics)Economic geographyRegional scienceAgricultureEnvironmental resource managementEcologyEnvironmental scienceArchaeology

Résumé

récupéré en direct d'OpenAlex

The characterization of the dynamics of certain categories of land in respective landscapes, either with or without considering the latter, has been the focus of domestic and foreign scholars. It should be noted that the former has become intensively interested in this issue relatively recently, since the twenty-first century. In particular, over the past 10 years, O. Butrym has assessed the structure of the land fund with the determination of the peculiarities of its use based on the analysis of the level of anthropogenic load and environmental stability of the territory of the Kyiv oblast, and substantiated the directions of improving the ecological balance of land use in the region. The current state and peculiarities of the use of the land fund of the Vinnytsia region, its component structure, and peculiarities of its territorial differentiation are examined in the collective work of O. Sukhyi, K. Darchuk, N. Zelena. The results of the current state and use of land resources in the Khmelnytskyi oblast, where the largest areas are occupied by agricultural land, are presented by V. Lapchynskyi and O. Boiko. The basic principles of optimizing the landscapes of Central Podillia based on the study of the structure and dynamics with a scheme of rational organization of their territory were proposed by L. Kostiv in her dissertation study. Abroad, the issue of temporal dynamics of the main categories of land in the physiographic region has been prominent since the second half of the twentieth century. A study of a series of 19 maps by land use categories was developed by R. Dolan, B. Hayden, and C. Vincent in the 1970s. They provided recommendations for a remote sensing system for monitoring the coastal zone in the context of the dynamics of their studied landscapes. Monica Goigel Turner developed spatial simulation models based on historical aerial photography to predict temporal changes in land use patterns in the foothill district of Georgia (USA) in five land use categories: urban, agricultural, deserted, pasture, and forest. Changes in land use and landscape structure in the agricultural landscape in the central Czech Republic were studied by Z. Lipský. He paid special attention to the major changes that occurred during the 40 years of socialist collectivism. The site of intensive tripartite land use between urban, agricultural, and natural use and its derived problems near Niagara Falls (Ontario) for the period 1935–1981 was studied by Michael R. Muller and John Middleton. We assessed the temporal dynamics of the main categories of land in the territory of 24 physical-geographical regions, which are part of 6 physical-geographical sections: Prut-Dniester upland forest-steppe region, Prut-Siret upland forest-meadow region, Skyba mid-mountain forest Carpathians, Verkhovyna lowland forest-meadow Carpathians, Polonynian-Chornohora subalpine forest Carpathians, and Marmarosh mid-mountain subalpine forested Carpathians. The characterization of each of the 24 aforementioned physical-geographical regions aims to consider changes in anthropogenic pressure on each component of the land fund by type of use and management during the 2004–2016 period. Our goal is also to identify trends that may lead, given the specified method of use, to changes in certain ecosystems or loss of the landscape's original appearance. The temporal dynamics of the main categories of land were considered in the territory of 24 physical-geographical regions, which are part of 6 agricultural sections. The Prut-Dniester upland region, situated as a watershed between the Dniester and Prut river systems and characterized by a flat forest-steppe landscape type, is the most developed agricultural region and exhibits a clearly defined agricultural specialization. In general, the analysis indicates a tendency of clear dominance of two types of land in the land structure of each of the physical-geographical regions – agricultural and forested. The Prut-Dniester Upland Forest-Steppe Region stands out within the generalized group of agricultural and forest lands. Among the 8 districts of this region, 3 (Dolyniany-Balkivtsi, Zastavna, and Oselivka) have a very high share of agricultural land: 87 %, 85 %, and 83 %, respectively. The next subgroup with large values of agricultural land includes 3 more districts of the Prut-Dniester region: Kelmentsi, Novoselytsia, and Kitsman, where agricultural land is utilized on 75–79 % of their territory. The third subgroup in Prut-Dniester is formed by Khotyn and Sokyriany physical-geographical regions with an almost balanced structure of agricultural and forest land. Starting from the Skyba mid-mountain forested Carpathians and upwards, the ratio of agricultural and forest land varies in places. In 5 physical-geographical regions of the Carpathians (Berehomet, Shurdyn, Maksymets, Yarovytsia, Chornodil), the share of forests is very high – more than 75.1 %. The share of agricultural land in all these physical-geographical regions is less than 33.2 %. The exceptions are Putyla and Cheremosh, where the share of agricultural land is the highest (34 %) among all the highland regions. Thus, 11 regions have the largest relative shares (> 75.1 %) of agricultural (6 regions) and forest (5 regions) land. The combined total structural analysis of the lands in Chernivtsi Oblast's physical-geographical regions revealed the presence of a formed Dniester-Prut-Siret macro-centre, a Sokyriany mini-centre with a positive land structure, and the Polonyna-Chornohora-Marmaros physical-geographical region centre with a negative land ratio. The Dniester-Prut-Siret mega-centre encompasses approximately 50.9 % of the area of Chernivtsi Oblast, within which the Brusnytsia physical-geographical region, with a satisfactory land structure, is located as an island element. The Sokyriany mini-centre, exhibiting a good-to-best combination of land, is situated in the extreme east of Chernivtsi Oblast and covers 15.2 % of its area. The Polonyna-Chornohora-Marmaros physical-geographical region centre, characterized by poor and worst land structure, accounts for 33.9 % of the territorial area. Keywords: physiographic region, land structure, rating analysis, rating evaluation.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,005
Communication savante0,0000,000
Science ouverte0,0010,001
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 Opus0,006
Tête enseignante GPT0,200
Écart entre enseignants0,193 · 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.

Devis d'étudeObservationnel
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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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