The changing Arctic Ocean: consequences for biological communities, biogeochemical processes and ecosystem functioning
Bibliographic record
Abstract
The Arctic region is undergoing some of the most rapid rates of climate change in the world [1], with dramatic transformations underway in terrestrial, coastal and offshore environments that have immediate and long-term consequences for socio-ecological systems (e.g. [2–5]). Significant changes in the type, extent and thickness of ice cover [6], meltwater input [7] and water mass dynamics [8], coupled with warming and ocean acidification [9], have already begun to impact ecosystem processes and the flora and fauna that inhabit a range of Arctic habitats [10]. The pace of change is such that our understanding of the way in which Arctic systems are structured and function is outdated, and insufficient to inform management, mitigation and adaptation efforts across the region [11,12]. Projections indicate that, even if global stabilization of temperature below 1.5°C is realized, changes will continue to manifest over an extended period, perhaps even millennial timescales [13] and may include unprecedented shifts in structure [14]. Changes to key components of Arctic ecosystems are already occurring, yet the collated evidence of how changes to baseline conditions are proceeding across the Arctic Ocean is still poorly constrained [15], focused on a limited number of exemplar areas [16], and seldom adopts a holistic view that begins to provide a nuanced understanding of the modus operandi of the Arctic [17]. This is concerning because informed decision- and policy-making benefits from a broad understanding of system dynamics, including feedbacks and the likelihood of ecological surprises [18], yet the focus of study is already shifting from the natural sciences to social sciences and humanities to meet legislative and policy demands [19]. Now more than ever, foundational concepts and evidence are needed to support sustainable management and policy, preferably with a focus on continually acquiring, interpreting and applying new interdisciplinary knowledge to enhance understanding [20].
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".