Towards integrated knowledge of climate change in Arctic marine systems: a systematic literature review of multidisciplinary research
Bibliographic record
Abstract
Climate change affects Arctic marine ecosystems, the ecosystem services they provide, and the human well-being that relies on these services. The impacts of climate change in the Arctic and elsewhere involve cascading effects and feedbacks that flow across social-ecological systems (SES), such as when sea ice loss alters food security through changes in the distribution of marine animals. These cascades and feedbacks across social and ecological systems can exacerbate the effects of climate change or lead to surprising outcomes. Identifying where cascades and feedbacks may occur in SES can help anticipate, or even prevent unexpected outcomes of climate change, and lead to improved policy responses. Here, we perform a systematic literature review of multidisciplinary Arctic research to determine the state of knowledge of the impacts of climate change on marine ecosystems. Then, in a case study corresponding to Inuit regions, we use network analysis to integrate research into a SES perspective and identify which linkages have been most versus least studied, and whether some potential cascades and feedbacks have been overlooked. Finally, we propose ways forward to advance knowledge of changing Arctic marine SES, including transdisciplinary approaches involving multiple disciplines and the collaboration of Indigenous and local knowledge holders.
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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".