Ocean acidification through the lens of ecological theory
Bibliographic record
Abstract
Ocean acidification, chemical changes to the carbonate system of seawater, is emerging as a key environmental challenge accompanying global warming and other human-induced perturbations. Considerable research seeks to define the scope and character of potential outcomes from this phenomenon, but a crucial impediment persists. Ecological theory, despite its power and utility, has been only peripherally applied to the problem. Here we sketch in broad strokes several areas where fundamental principles of ecology have the capacity to generate insight into ocean acidification's consequences. We focus on conceptual models that, when considered in the context of acidification, yield explicit predictions regarding a spectrum of population- and community-level effects, from narrowing of species ranges and shifts in patterns of demographic connectivity, to modified consumer-resource relationships, to ascendance of weedy taxa and loss of species diversity. Although our coverage represents only a small fraction of the breadth of possible insights achievable from the application of theory, our hope is that this initial foray will spur expanded efforts to blend experiments with theoretical approaches. The result promises to be a deeper and more nuanced understanding of ocean acidification'and the ecological changes it portends.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".