Long-term ocean and resource dynamics in a hotspot of climate change
Bibliographic record
Abstract
The abundance, distribution, and size of marine species are linked to temperature and nutrient regimes and are profoundly affected by humans through exploitation and climate change. Yet little is known about long-term historical links between ocean environmental changes and resource abundance to provide context for current and potential future trends and inform conservation and management. We synthesize >4000 years of climate and marine ecosystem dynamics in a Northwest Atlantic region currently undergoing rapid changes, the Gulf of Maine and Scotian Shelf. This period spans the late Holocene cooling and recent warming and includes both Indigenous and European influence. We compare environmental records from instrumental, sedimentary, coral, and mollusk archives with ecological records from fossils, archaeological, historical, and modern data, and integrate future model projections of environmental and ecosystem changes. This multidisciplinary synthesis provides insight into multiple reference points and shifting baselines of environmental and ecosystem conditions, and projects a near-future departure from natural climate variability in 2028 for the Scotian Shelf and 2034 for the Gulf of Maine. Our work helps advancing integrative end-to-end modeling to improve the predictive capacity of ecosystem forecasts with climate change. Our results can be used to adjust marine conservation strategies and network planning and adapt ecosystem-based management with climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".