Bibliographic record
Abstract
The decline of biodiversity is affecting our everyday life, we currently get 75% of the vitamins and nutrients we consume from crops with animal pollinators, but many of these pollinators are in danger due to declines in insect populations. The global decline of pollinators is symptomatic of a much larger global trend: the rapid acceleration of species extinction rates. In this chapter, Smith and Daily emphasize that the loss of biodiversity is not just due species decline, but also radical landscape transformation. The biggest transformation is the rise of urban areas, with 60% of the human population living in cities with very limited biodiversity. Protected areas have played a central role in conservation since the late nineteenth century, and the designation of such areas has only intensified since the 1970s, with calls for 30% protection by 2030. However, this is not enough to support the biodiversity upon which human society depends. Acts like the Endangered Species Act or the Clean Air Act, introduced in the 1970s, underscored the need for biodiversity to be protected globally, and not just in the isolated pockets of designated protected areas. It is suggested that current government action to increase biodiversity is significant, but not sufficient – we must start seek to implement sustainability at every level in our economic system. Planet Earth’s species, habitats, ecosystems and landscapes are fundamental to who we are as human beings, and we must act quickly.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.031 |
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".