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Record W2279223568 · doi:10.1016/j.cliser.2016.01.002

Implications of 2 °C global warming in European summer tourism

2016· article· en· W2279223568 on OpenAlexaff
Manolis Grillakis, Aristeidis Koutroulis, Konstantinos Seiradakis, Ioannis K. Tsanis

Bibliographic record

VenueClimate Services · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsMcMaster University
FundersSeventh Framework ProgrammeEuropean Commission
KeywordsTourismClimate changeGeographyGlobal warmingMediterranean climateClimatologyClimate modelPhysical geographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Tourism is highly dependent on the climatic conditions of a given destination. This study examines the impact of two degrees global warming on European summer tourism from a climate comfort perspective. The changes in summer tourism climate comfort are realized with the aid of the Tourism Climatic Index (TCI). Four ENSEMBLES Regional Climate Models (RCMs) provided the data for Europe under the A1B emission scenario that are used in the analysis of potential changes in tourism favorability. Results show that the change in climate will positively affect central and northern Europe, increasing the potential of further economic development in this direction. Mediterranean countries are likely to lose in favorability during the hot summer months whereas will tend to become more favorable in the early and late summer seasons. Considering that the two degrees period is focused between 2031 and 2060, the estimated shifts in the climate favorability of Mediterranean countries indicate a need in early adaptation strategies.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.033
GPT teacher head0.354
Teacher spread0.321 · 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 designSimulation or modeling
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

Citations69
Published2016
Admission routes1
Has abstractyes

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