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Record W2350310057

The Impact of the Arctic Climate Change on Canadian and Chinese Society and Economy

2010· article· en· W2350310057 on OpenAlexaboutno aff
Liu Yi-gong

Bibliographic record

VenueJournal of Inner Mongolia University · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changePolitical economy of climate changeGlobal warmingArcticViewpointsEcological forecastingChinaPolitical scienceSovereigntyEffects of global warmingPoliticsEconomyGeographyPolitical economySociologyEconomicsEcologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Climate change causes natural resource environmental changes,and further affects social and economic developments.The Arctic climate change is the most evident among the global climate changes,and has begun to impact on ecological change.In this pure land the decrease of the Arctic sea ice has made summer navigations and resource explorations become potential engineering behaviors,a fact which has forced many countries to exert all their strength to claim the sovereignty of the Arctic Ocean.This essay deals with the influence of the Arctic climate change on Canadian society and economy,and with the aspects of global warming that Canadian political class,scientific circles,engineering field,and the common folk focus on,together with the measures that they would take to react to the climate change.Also,according to the situation in both China and Canada,from the angle of literature this essay makes a brief analysis of attitudes and different viewpoints by the two countries in ideology,hoping that domestic and foreign colleagues will use it for reference.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.566

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0140.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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