Environmental status of Svalbard coastal waters: coastscapes and focal ecosystem components (SvalCoast)
Bibliographic record
Abstract
This is chapter 6 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). Coastal waters are among the most productive regions in the Arctic. These nearshore waters are critical breeding and foraging grounds for many invertebrates, fish, birds, and marine mammals and provide a host of ecosystem services, from private outdoor activities to large-scale tourism and fisheries. Arctic nature coast types (= coastscapes) and biodiversity are under growing pressure as climate change and human activities increase in the region. More data on the rates of change in the physical, chemical and biological environments in these highly dynamic and heterogeneous coastscapes are urgently needed. Svalbard is warming more rapidly than anywhere else in the Arctic, and the Arctic is warming at 2-3 times the rate of other areas globally. Svalbard experiences steep climate gradients due to being at the interface between warm Atlantic and cold Arctic waters. Warming is creating a huge potential for increased colonisation by boreal species, with potential negative impacts on “native” species assemblages and food webs. Changes in physical drivers and biodiversity patterns must be documented to predict upcoming challenges and opportunities as the Arctic changes. This synopsis is the first joint effort across nations, institutes, and disciplines to address current gaps in knowledge and monitoring of Svalbard’s coast – a result of an international workshop Svalbard Sustainable Coasts in Longyearbyen, February 2020. Another important task of this synthesis work was to look into the applicability of the defined coastscapes and biodiversity tools in the Arctic Coastal Monitoring plan, initiated by the Arctic Council’s Conservation of Arctic Flora and Fauna (CAFF, www.caff.is), for Svalbard.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".