Arctic biodiversity responses to climate change impacts in the Canadian Beaufort Sea 
Bibliographic record
Abstract
The Arctic is experiencing unprecedented rates of warming. Arctic coastal environments are particularly vulnerable to the consequences: thawing of permafrost, decline of sea ice, and increased fluxes of sediment, organic carbon and nutrients across the land-ocean interface. These effects of global climate change drive significant transformations in coastal biogeochemistry and ecosystems, with severe implications for local communities. However, the responses of nearshore Arctic ecosystems to these changes, as well as involved mechanisms and driving forces, remain poorly constrained. The 'Fluxes from Land to Ocean: How Coastal Habitats in the Arctic Respond' (FLO CHAR) project focuses on the Mackenzie Delta region of the Beaufort Sea and asks the question: How does modern climate change alter land-ocean dynamics and the biodiversity of coastal ecosystems? A key objective is to explore biodiversity shifts and ecosystem functioning over the past millennium, to gain long-term perspectives of ecosystem dynamics in response to climate-driven changes. This is achieved through marine sedimentary ancient DNA (sedaDNA) analyses, utilizing state-of-the-art metabarcoding approaches and shotgun metagenomics. Establishing baseline data of coastal biodiversity in the Beaufort-Mackenzie region during the Late Holocene will allow to put modern biodiversity and ecosystem dynamics in a long-term context. Further, key diversity shifts will be assessed in the context of paleoenvironmental and -geochemical records to assess potential responses to climate change impacts, such as sea ice dynamics and land-ocean organic matter fluxes. The outcomes of the project will offer a critical framework for assessing future directions of Arctic coastal environments, and developing sustainable management and adaptation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".