MétaCan
Menu
Back to cohort
Record W6893229201 · doi:10.5281/zenodo.14712269

Region constrained terrestrial areas of conservation importance for 2030

2024· dataset· en· W6893229201 on OpenAlexaboutno aff

Bibliographic record

VenueIIASA PURE (International Institute of Applied Systems Analysis) · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsBiomeGridSnapshot (computer storage)PrioritizationMap projectionSpatial analysisLand coverBiodiversity

Abstract

fetched live from OpenAlex

This data repository contains updated layers of global terrestrial conservation importance based on the framework and feature data described in Jung et al. (2021). The purpose of these updated maps is to more closely support modelling efforts in global intercomparison exercises (e.g. Bending the Curve) as well as to align with Target 3 ("30x30") of the Kunming-Montreal Global Biodiversity Framework (source).Compared to the previous results published in Jung et al. (2021), a few updates have been made: The protected area dataset was updated to a WDPA snapshot collated in late November 2024. No hierachical rankings, instead only the solution for 30% global land "budget" are provided. Additional variants (see accompanying variant file) related to national responsibility and a more globally just distribution are provided. Specifically two variants that constrain the maximum allocation of land to 30% per country or 30% of terrestrial biome The global spatial-explicit database on Other Effective Conservation Areas (OECM) is considered in some variants so as to nudge (if there are sufficient benefits) areas of conservation importance towards those places where such management occurs. Data properties: Format Gridded geoTiff Spatial grain 10 x 10 km² and 50 x 50 km² Units Fractions of grid [0-1] x 10000 Geographic projection World Mollweide (https://epsg.io/54009) Reference period 2024 (WDPA) Spatial extent Global (excluding Antarctica) Number of variants / prioritization scenarios 18, See accompanying inventory.xlsx file. Additionally, a reporting protocol following the ODPSCP standard version 0.4 (see Jung et al.) is provided as overview and to allow relative comparisons with other prioritizations. Cautionary notes for interpretation: This analysis reveals areas of conservation importance, defined as those areas which would benefit from increased conservation actions (through whatever measure). They are not prescriptive with regards to the type and governance of conservation (e.g. protected areas) for any given area. This work builds primarily on the processed feature data (biodiversity and carbon) used in Jung et al. (2021). Since then several updates to IUCN range data have been made, which however are not considered in this work. Habitat changes since the production of the feature data, for example owing to land-use changes such as deforestation, were not considered or incorporated. Biodiversity data originates primarily from expert-based range maps, which can be prone to omission and comission errors. Although care was undertaken to geographically project and rasterize the protected area dataset to a gridded resolution, it is possible that not every protected area is captured in whole. There are known issues with publicly available WDPA data not being available for several countries including China (see protectedplanet.net and Bingham et al. 2019). Region-constrains added for those countries in particular thus likely result in more ambitious areas than what already is under conservation management. Several countries (but by far not all) have already made detailed commitments as part of National Biodiversity Strategies and Action Plans (NBSAPs) on which type and how much area they plan to conserve. These were however not taken into account for this work. Disclaimer: Any borders of countries used here as constraint do not necessarily represent the view of IIASA or it's National Member Organizations. All layers are provided as is and the authors takes no responsibility for errors or misuse and misinterpretation. References: Jung, M., Arnell, A., de Lamo, X. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat Ecol Evol 5, 1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7Jung, M., Arnell, A., De Lamo, X., García-Rangelm, S., Lewis, M., Mark, J., Merow, C., Miles, L., Ondo, I., Pironon, S., Ravilious, C., Rivers, M., Schepashenko, D., Tallowin, O., van Soesbergen, A., Govaerts, R., Boyle, B. L., Enquist, B. J., Feng, X., … Visconti, P. (2021). Areas of global importance for conserving terrestrial biodiversity, carbon, and water (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5006332

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.021

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.294
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIIASA PURE (International Institute of Applied Systems Analysis)French-language works237,207