Leveraging big Earth data for spatially explicit tracking of the progress on UN SDG15.1.2
Bibliographic record
Abstract
The UN SDG15 'Life on Land,' aims to promote the sustainable management and use of terrestrial ecosystems, with sub-indicator SDG15.1.2 quantifying the proportion of Key Biodiversity Areas (KBAs) covered by protected areas. However, progress on SDG15.1.2 remains unclear, complicating the prioritization of ecosystems with high conservation potential. Here, we propose an innovative framework that utilizes Big Earth Data (BED) to quantify SDG15.1.2 across five mainland Southeast Asian (MSA) countries. This framework employs the Integrative Multidimensional Biodiversity Index (iMBI) to map KBAs, enabling the derivation of SDG15.1.2 by overlaying KBA maps with protected areas. The results indicate that Cambodia (87.3%) and Thailand (63.9%) have relatively high SDG15.1.2, while Myanmar (13%), Vietnam (23.3%), and Laos (25.1%) exhibit considerably lower values, resulting in a regional average of 29.1% for the MSA. While there was a slight upward trend in SDG15.1.2 from 2000 to 2020, the rate of increase remains insufficient to achieve comprehensive legal protection for the majority of KBAs by 2030. Furthermore, we identified areas with high conservation potential that remain unprotected, providing insights for improving SDG15.1.2. Although the MSA serves as a case study, the proposed framework is adaptable to other regions, facilitating consistent and spatially explicit global tracking of UN SDG15.1.2.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".