Understanding biodiversity – ecosystem service linkages in real landscapes
Bibliographic record
Abstract
Human domination of the biosphere has profoundly transformed terrestrial and aquatic landscapes across scales (Díaz et al. 2019 ). One of the fundamental consequences of pervasive human-induced environmental changes is the massive and accelerated loss of biological diversity–declines in the variety of microbes, plants, and animals in land and water that have evolved over the last 3.6 billion years on the planet. A comprehensive global assessment has revealed that over 75% of species have been lost in the most severely human-impacted ecosystems on the planet (Newbold et al. 2015 ), and current rates of species extinction are ~ 100 to 1,000 times outpacing the background rates observed in the fossil record (Pimm et al. 2014 ). Similarly, an updated planetary boundary analysis has demonstrated that biosphere integrity that encompasses genetic diversity is among the six boundaries that has transgressed its safe operating space for humanity (Steffen et al. 2015 ; Richardson et al. 2023 ). If current trends of human pressure and biodiversity loss continue, projections suggest that the Earth may face its sixth mass extinction in 350 to 500 years from now (Barnosky et al. 2011 ).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".