Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Because of human action, the Earth has entered an era where profound changes in the global environment are creating novel conditions that will be discernable far into the future. One consequence may be a large reduction of the Earth's biodiversity, potentially representing a sixth mass extinction. With effective stewardship, the global change drivers that threaten the Earth's biota could be alleviated, but this requires clear understanding of the drivers, their interactions, and how they impact ecological communities. This review identifies 10 anthropogenic global change drivers and discusses how six of the drivers (atmospheric CO 2 enrichment, climate change, land transformation, species exploitation, exotic species invasions, eutrophication) impact Earth's biodiversity. Driver impacts on a particular species could be positive or negative. In either case, they initiate secondary responses that cascade along ecological lines of connection and in doing so magnify the initial impact. The unique nature of the threat to the Earth's biodiversity is not simply due to the magnitude of each driver, but due to the speed of change, the novelty of the drivers, and their interactions. Emphasizing one driver, notably climate change, is problematic because the other global change drivers also degrade biodiversity and together threaten the stability of the biosphere. As the main academic journal addressing global change effects on living systems, GCB is well positioned to provide leadership in solving the global change challenge. If humanity cannot meet the challenge, then GCB is positioned to serve as a leading chronicle of the sixth mass extinction to occur on planet Earth.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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 it