Sufficiency and Suitability of Global Biodiversity Indicators for Monitoring Progress to 2020 Targets
Bibliographic record
Abstract
Abstract Biodiversity indicators are widely used tools to help determine rates of biodiversity change and the success or failure of efforts to conserve it. However, their sufficiency and suitability in providing information for decision‐makers is unclear. Here, we review the indicators brought together under the Biodiversity Indicator Partnership to monitor progress towards the Aichi Targets to determine where there are gaps. Of the 20 Aichi Biodiversity Targets, Targets 2, 3, and 15 are missing indicators entirely. Scoring the indicators in relation to their alignment, temporal relevance and spatial scale shows additional gaps under Targets 1, 13, and 16–20. Predominately, gaps were found to be socio‐economic in nature (i.e., benefits, pressures, and responses) rather than status‐related (i.e., states), principally due to a poor alignment between the indicator and the text of the Aichi Target. Hence, it is critical that existing indicators are properly resourced and maintained and new indicators developed to be able to effectively monitor biodiversity and its influencing factors to 2020 and beyond.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".