Notice bibliographique
Résumé
As urbanization rates around the world continue to climb, especially in developing nations, the agricultural sector is being left behind. It is necessary to examine the direct and indirect effects that urbanization has on farmland surrounding mega cities, and how local governments can adapt to avoid conflict. Urbanization and urban sprawl take over arable farmland in order to start development of housing, commercial, or recreational land. These new developments also hike up the land price of local small-scale farms in the area and make owning land an extreme privilege. The main goal of this paper is to establish the relationship between urbanization and agriculture, and examine its effects on the sector, via farmland loss, land price, and food costs. Many farms around major cities have a continuous loss in the number of farms in their vicinity. In addition to this, the ownership of these agricultural plots is decreasing, as small-scale farms are only able to rent the land. Results led to a conclusion that urbanization and urban sprawl do in fact have a serious effect on the agricultural sector that surrounds the city limits, and recommendations such as the Greenbelt Plan that took effect in 2005 should be implemented in cities suffering from this issue Through much debate in the early 21st century, the establishment of the Greenbelt Plan, which protects farmland and greenspace around the city of Toronto acted as a massive success, and one that can be viewed as a proper mitigation outcome. This implementation of a Greenbelt is a proper solution in allowing for both agricultural lands, the ecosystem and biodiversity to thrive in the face of urbanization. Studying the impact of the Greenbelt is especially important in developing countries, as well as countries currently experiencing problems with urbanization and urban sprawl. The cases of Turkey and Pakistan experienced much of the same issues Toronto saw in the early 21st century and are now struggling to identify mitigation solutions to stop the spread of arable farmland into developments such as housing and commercial. The agricultural sector is slowing decreasing in number of farms as family owned, and small-scale operations are getting more and more difficult to operate around the world, especially in the vicinity of large cities experiencing a population boom.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; un appel candidat d’une seule tête enseignante, 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 ».