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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
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