Mapping the planet's critical areas for biodiversity and people
Notice bibliographique
Résumé
Data associated with "Mapping the planet's critical areas for biodiversity and people" Abstract: Meeting global commitments to conservation, climate, and sustainable development goals requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature’s contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving 44% of global land area through protection or sustainable management could provide 90% of current levels of ten of nature’s contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Framework for Biodiversity to conserve at least 30% of global lands and waters. More than one-third of areas required for conserving nature’s contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals. This dataset contains outputs of spatial optimizations run using prioritizr (https://prioritizr.net/index.html) on February 27 2022. Data includes raster files (TIF format). Raster values are 0-1, where 1 means the grid cell was selected to achieve a particular target, 0 means the grid cell was not selected, and values between 0 and 1 indicate a grid cell was partially selected. Three variations of the spatial optimization were run. Each zip file contains the outputs from one of these variations: NCP (Nature's contributions to people) only File name: NCP_only_2km.zip NCP and biodiversity, prioritization run at 10km then masked to natural and semi-natural habitat at 2km File name: NCP_biod_nathab2.zip NCP and biodiversity, with protected areas and OECM (WDPA) locked in File name: NCP_biod_WDPA_nathab.zip Within each variation, 19 different spatial optimizations were run, with NCP targets ranging from 5%-95% in 5% increments. Raster filenames within ZIP files indicate the NCP (ecosystem service) target (for example, es05 indicates a target of 5%) Whether biodiversity was included or not (for example, bio1 indicates biodiversity was included, bio0 indicates it was not) Two additional files were included, which are the result of summing the rasters from the above scenarios. Raster values range from 0-19, where 19 indicates grid cells selected in all scenarios, 0 indicates grid cells selected in 0 scenarios. Higher values (e.g. 19) indicate cells with the highest levels of NCP globally in the least amount of area. NCP_only_2km_sum - NCP only scenario, all rasters summed. NCP_biod_nathab_sum - NCP and biodiversity scenario, masked to natural habitat, all rasters summed. Additional files include: dpi.tif - Development Potential Index raster dpi_key.csv - legend describing the DPI raster values HDP_DriverCats.tif - High Development Potential areas disaggregated by sector (raster) HDP_DriverCats_key.csv - legend describing the HDP raster values es90bio1_hdp_drivers_multiply.tif - raster resulting from the combination of the prioritized areas for NCP and biodiversity combined with High Development Potential areas for each economic sector (key is the same as for HDP raster) nathab_2km_WGS84.tif - raster with natural and semi-natural habitat mask (based on ESA 2015 land cover) (2 km)
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,010 |
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 ».