MétaCan
Menu
Back to cohort
Record W3047387111 · doi:10.1080/03088839.2020.1803430

Climate change adaptation by ports: the attitude of Chinese port organizations

2020· article· en· W3047387111 on OpenAlexaff
Yufeng Lin, Adolf K.Y. Ng, Anming Zhang, Yimeng Xu, Yile He

Bibliographic record

VenueMaritime Policy & Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsPort (circuit theory)Adaptation (eye)Context (archaeology)Climate changeChinaBusinessClimate change adaptationEnvironmental resource managementPolitical scienceEconomicsEngineeringGeographyPsychology

Abstract

fetched live from OpenAlex

Climate change poses a potential risk to coastal infrastructure, thus threatening the economics or even the safety of human beings. Thus, a better understanding of the attitude of port organizations toward climate adaptation and mitigation is essential. This paper addresses this research gap by investigating 18 port organizations in China. The questions include the impediments and the impact of context, systems, and other factors on the implementation of adaptation strategies. The results indicate that port organizations are generally aware of climate change impacts and agree that some further steps are needed. However, policy support serves as a key factor in implementing adaptation plans. Apart from offering important insight on the attitude of port organizations, the study also serves as a platform for further research on climate adaptation planning in China.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMaritime Policy & ManagementSame topicMaritime Transport Emissions and EfficiencyFrench-language works237,207