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Record W4391375668 · doi:10.18280/ijsdp.190138

The Impact of Climate Change on National Security

2024· article· en· W4391375668 on OpenAlexvenueno aff
Ljupcho Sotiroski

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeNational securityEnvironmental resource managementEnvironmental planningEnvironmental scienceGeographyPolitical scienceGeology

Abstract

fetched live from OpenAlex

The purpose of the presented scientific work was: a historical review and identification of the main root causes of the emergence of threatening natural phenomena on the planet; outlining of key areas and sectors of the population's life; analysis of the influence of society and official authorities on the control and management of issues in the field of natural disasters; modelling of possible consequences and ways to solve the most threatening problems due to climate change.The main method of scientific cognition that was used in writing this article is the system-analytical method, with the help of which, using the processes of analysis and modelling, the causes were identified, the directions of direct impact were outlined and logical alternatives and solutions to the problems that have arisen against the background of climate change and natural disasters were provided.The main results obtained during the study of the presented topics are as follows: the main causes of the onset of irreversible climate change on the planet are identified; the most vulnerable to natural anomalies areas of public and state direction are highlighted; common methods and means of climate change control are studied.The authors conclude that future climate change impacts will intensify unless mitigation efforts are increased, thereby not only neutralizing, but also potentially benefiting, humanity and Earth's future.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.085
GPT teacher head0.375
Teacher spread0.290 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicClimate Change, Adaptation, MigrationFrench-language works237,207