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Record W4400134344 · doi:10.1002/sd.3108

Alignment of the 2030 Agenda to the port industry

2024· article· en· W4400134344 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSustainable Development · 2024
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversité du Québec à Chicoutimi
FundersMitacs
KeywordsCLARITYPort (circuit theory)Sustainable developmentSustainabilityIdentification (biology)Process managementBridge (graph theory)Relevance (law)BusinessSupply chainPolitical scienceEnvironmental resource managementEconomicsMarketingEngineering

Abstract

fetched live from OpenAlex

Abstract The United Nations' 2030 Agenda for Sustainable Development (2030 Agenda) serves as a global framework for addressing sustainability challenges. The port industry (PI) plays a crucial role in achieving the Sustainable Development Goals (SDGs) as a vital component of the global economy and supply chains. This research paper addresses the alignment between the PI and the 2030 Agenda. The study aims to bridge the research gap by exploring the extent to which the PI aligns with the SDGs and proposes a framework for implementation. Through an analysis of literature, this study identifies the relevant SDG targets for the PI. The original wordings of the relevant targets were adapted to make them meaningful to the PI. The adapted targets were validated by eight Canadian Port authorities to ensure their relevance and clarity. The alignment resulted in the identification of 69 targets, representing all 17 SDGs.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score0.322

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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