MétaCan
Menu
Back to cohort
Record W4411114914 · doi:10.14430/arctic81258

Canadian Coordination in Support of Sustained Observations of Arctic Change

2025· article· en· W4411114914 on OpenAlexvenueaboutno aff
M. S. Murray, Ravi D. Sankar, Lisa L. Loseto, Peter Pulsifer, Jackie Dawson

Bibliographic record

VenueARCTIC · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsArcticThe arcticOceanographyPhysical geographyGeographyClimatologyEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Over the past two decades, scientists, advocates, and communities have put considerable international effort into the development of a sustained Arctic observing system that can sufficiently monitor ongoing environmental and socio-economic change. Advances are slow due in part to a lack of nation-level coordination, with Canada being no exception. Canada needs a coordinated national strategy in support of sustained Arctic observations that will benefit all Canadians and the broader global community, advance Arctic system understanding, and support management and mitigation of the impacts of rapid Arctic transformation. This paper lays out a proposed framework for a coordinated national initiative in support of sustained Arctic observing that includes cross-sector and Indigenous co-developed and co-executed plan, plus an implementation strategy. Recommendations include: 1) establishing national teams (for observing, data, and infrastructure) to effectively deliver on our international obligations related to Arctic research, 2) supporting data sharing, and 3) ensuring sustained observations while also providing observational data and information in support of societal needs within Canada, including many identified in the Arctic and Northern Policy Framework and the National Inuit Strategy on Research.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.795

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueARCTICSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207