Mapping the planet's critical areas for biodiversity and people
Bibliographic record
Abstract
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)
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".