MétaCan
Menu
Back to cohort
Record W6979875199

Análisis de dominio sobre riesgos y clima en la Web of Science

2019· article· en· W6979875199 on OpenAlexaboutno aff

Bibliographic record

VenueE-LIS Repository (University of Naples Federico II) · 2019
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsBibliometricsClimate changeSubject (documents)ProductivityLatin AmericansImpact factorArcticDomain (mathematical analysis)Thematic map
DOInot available

Abstract

fetched live from OpenAlex

Design/Methodology/Approach: The study has a justified quantitative approach in the bibliometric methods and the social networks analysis. The Web of Science database allowed to recover the scientific production on Risks and Climate. Primary indicators are calculated, and multivariate representations of the domain are made. \nResults/Discussion: Increases in scientific output were identified in 2006 and 2008, where the variation rate shows its highest expression. There is high productivity and collaboration in the United States, England and Australia respectively, and the participation of Latin American countries in the scientific production of the subject was identified. James D. Ford and Tristan Pearce are the authors with the largest number of collaborative works (13 articles) on the topics of climate change in the Canadian Arctic and adaptation of the Eskimos. Environmental Sciences and Ecology (Environmental Science & Ecology) predominate in thematic categories. The most influential journals have an impact factor greater than 4. The most cited author is the Intergovernmental Panel on Climate Change (IPCC), the highly cited journals were: Climatic Change and Global Environmental Change-Human and Policy Dimensions. \nConclusions: Domain analysis reveals patterns that cannot be observed with the naked eye in the thinking and language of professional groups. Bibliometrics is the most widespread and used approach. The study allowed us to carry out an in depth analysis of the topic Risk and Climate, identifying the features that characterize it in the scientific production indexed in the Web of Science. \nOriginality/Value: It is a topic that worries the scientific community worldwide, based on the growing increase in articles on the subject. The study is a reference for future research on risks and climate. It meets a demand from the Coastal Ecosystem Research Center of Ciego de Ávila in Cuba, which needed to know the scientific production on this topic for its research and scientific development.

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)0.022
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.064
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0480.046
Science and technology studies0.0010.001
Scholarly communication0.0080.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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.

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueE-LIS Repository (University of Naples Federico II)Same topicScientific Research and TechnologyFrench-language works237,207