Notice bibliographique
Résumé
Global policy frameworks such as the UN Sustainable Development Goals (SDGs) or the Kunming-Montreal Global Biodiversity Framework (KMGBF) as well as numerous EU policies related to species and habitat conservation (e.g. Nature Restoration Law, Birds Directive, Habitats Directive, Water Framework Directive, Marine Strategy Framework Directive), ecosystem services (e.g. Pollinators Initiative, Land Use Land Use Cover and Forestry Regulation, proposed Forest Monitoring Regulation) and the sustainable management of natural resources (e.g. Common Fisheries Policy, Common Agricultural Policy) highlight the urgent need to monitor changes in biodiversity, ecosystems and the natural environment. However, tracking progress towards the ambitious policy goals is challenging and requires a minimum set of measurements that are consistent across scales and regions for deriving indicators that capture the major dimensions of change. Delivering such information is supported by the development of essential variables for climate (ECVs), oceans (EOVs), biodiversity (EBVs) and geodiversity (EGVs) which can be used to characterize and monitor changes on our planet. This can advance science and inform policy. Our knowledge, management and governance of the Earth system ultimately depends on diverse measuring tools and multiple data types, including remote sensing and in-situ data collection with field samples and experiments. For monitoring of biodiversity and ecosystems, EBVs can provide consistent knowledge about multiple dimensions of biodiversity change across space and time. For such variables, diverse data types are required, including structured in-situ observations, citizen science data, and time-series data collected through cutting-edge methods. These cutting-edge methods span DNA-based techniques like eDNA metabarcoding; digital sensors such as cameras, acoustic devices, and GPS tags; and remote sensing technologies, including satellites, drones, airplanes, and weather radars. In the era of “big data”, the vast and often unstructured datasets cannot easily be downloaded or analyzed without advanced, high-throughput processing pipelines. Transforming such data into actionable insights therefore involves applying FAIR (Findable, Accessible, Interoperable, Reusable) principles, integrating heterogeneous data from multiple sensors, testing the robustness and transferability of models and metric calculations, developing automated and transparent processing workflows, leveraging parallel or distributed computing, and employing cloud-based virtual research environments to streamline the analyses. These processed and standardized biodiversity data can be utilized for a variety of applications, including the construction of data cubes for spatiotemporal analysis, the building of models and tools for biodiversity and ecosystem change analysis, and simulations and scenarios using Digital Twins and other forecasting tools. Additionally, artificial intelligence (AI), particularly deep learning, has emerged as a powerful tool for analyzing big and complex datasets, such as imagery from satellites and unmanned aerial vehicles (UAVs), wildlife and insect camera images, acoustic recordings, and LiDAR point clouds. The integration of remote sensing and in situ observations, harmonized data and models, and the use of automatic recorders with AI algorithms will substantially advance biodiversity and ecosystem monitoring. This can provide improved support for species and habitat conservation policies and land use management, and enable science-driven strategies to address global environmental challenges.
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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 ».