MétaCan
Menu
Back to cohort
Record W3209322890 · doi:10.5281/zenodo.4293849

Environmental status of Svalbard coastal waters: coastscapes and focal ecosystem components (SvalCoast)

2021· article· en· W3209322890 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsEcosystemGeographyEnvironmental resource managementOceanographyEnvironmental scienceFisheryEnvironmental protectionPhysical geographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.036
GPT teacher head0.262
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it