Bibliographic record
Abstract
We discuss three interlinked issues: the natural pace of environmental change and adaptation, the likelihood that a population will adapt to a potentially lethal change, and adaptation to elevated CO2, the prime mover of global change. Environmental variability is governed by power laws showing that ln difference in conditions increases with ln elapsed time at a rate of 0.3-0.4. This leads to strong but fluctuating selection in many natural populations.The effect of repeated adverse change on mean fitness depends on its frequency rather than its severity. If the depression of mean fitness leads to population decline, however, severe stress may cause extinction. Evolutionary rescue from extinction requires abundant genetic variation or a high mutation supply rate, and thus a large population size. Although natural populations can sustain quite intense selection, they often fail to adapt to anthropogenic stresses such as pollution and acidification and instead become extinct.Experimental selection lines of algae show no specific adaptation to elevated CO2, but instead lose their carbon-concentrating mechanism through mutational degradation. This is likely to reduce the effectiveness of the oceanic carbon pump. Elevated CO2 is also likely to lead to changes in phytoplankton community composition, although it is not yet clear what these will be. We emphasize the importance of experimental evolution in understanding and predicting the biological response to global change. This will be one of the main tasks of evolutionary biologists in the coming decade.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".