Four challenges to an effective national nature assessment
Bibliographic record
Abstract
Comprehensive biodiversity assessments play an essential role in strengthening global and national conservation strategies. The recently announced first U.S. National Nature Assessment (NNA) provides an unparalleled opportunity to comprehensively review status and trends of biodiversity at all levels. This broad context can help in the coordination of actions to conserve individual species and ecosystems. The scientific assessments that informed the Kunming-Montreal Global Biodiversity Framework adopted at the 2022 Convention on Biological Diversity (CBD) conference of parties provide models for synthesizing information on trends at multiple levels of biodiversity, including decline in abundance and distribution of species, loss of populations and genetic diversity, and degradation and loss of ecosystems and their services. The assessments then relate these trends to data on drivers of biodiversity loss and pathways to their mitigation. The U.S. NNA can augment such global analyses and avoid the pitfalls encountered by previous U.S. efforts by ensuring policy-relevant design, data accessibility, and inclusivity in process and product and by incorporating spatial data relevant to national and subnational audiences. Although the United States is not formally a CBD party, an effective NNA should take full advantage of the global context by including indicators adopted at the 2022 meeting and incorporating an independent review mechanism that supports periodic stocktaking and ratcheting up of ambition in response to identified shortfalls in stemming biodiversity loss. The challenges to design of an effective U.S. assessment are relevant globally as nations develop assessments and reporting to support the new global biodiversity framework's targets. By considering and incorporating the diverse ways in which society values and benefits from nature, such assessments can help bridge the gap between research and conservation practice and communicate the extent of the biodiversity crisis to the public, fostering broad-based support for transformative change in humanity's relationship to the natural world.
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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 | 0.144 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".