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Record W2797374624 · doi:10.1080/08920753.2018.1451731

Port Decision Maker Perceptions on the Effectiveness of Climate Adaptation Actions

2018· article· en· W2797374624 on OpenAlexaff
Adolf K.Y. Ng, Huiying Zhang, Mawuli Afenyo, Austin Becker, Stephen Cahoon, Shu‐Ling Chen, Miguel Esteban, Claudio Ferrari, Yui‐yip Lau, Paul Tae‐Woo Lee, Jason Monios, Alessio Tei, Zaili Yang, Michele Acciaro

Bibliographic record

VenueCoastal Management · 2018
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAdaptation (eye)PerceptionClimate changePort (circuit theory)SkepticismEnvironmental resource managementProcess (computing)Climate change adaptationDecision makerEnvironmental planningBusinessComputer scienceManagement scienceGeographyPsychologyEnvironmental scienceEconomicsEngineeringEcology

Abstract

fetched live from OpenAlex

Effective adaptation to climate change impacts is rapidly becoming an important research topic. Hitherto, the perceptions and attitudes of stakeholders on climate adaptation actions are under researched, partly due to the emphasis on physical and engineering aspects during the adaptation planning process. Building on such considerations, the paper explores the perceptions of port decision makers on the effectiveness of climate adaptation actions. The findings suggest that while port decision makers are aware of potential climate change impacts and feel that more adaptation actions should be undertaken, they are skeptical about their effectiveness and value. This is complemented by a regional analysis on the results, suggesting that more tailor-made adaptation measures suited to local circumstances should be developed. The study illustrates the complexity of climate adaptation planning and of involving port decision makers under the current planning paradigm.

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)0.010
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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 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

Citations57
Published2018
Admission routes1
Has abstractyes

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