MétaCan
Menu
Back to cohort
Record W4206574901 · doi:10.3390/su14010373

Evaluating the Efficacy of Sustainability Initiatives in the Canadian Port Sector

2021· article· en· W4206574901 on OpenAlexafffundabout
Jennifer L. MacNeil, Michelle Adams, Tony R. ‎Walker

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityPort (circuit theory)Context (archaeology)Environmental resource managementBusinessSustainable developmentEnvironmental planningEnvironmental impact assessmentResource (disambiguation)EngineeringGeographyEconomicsPolitical scienceEcologyComputer science

Abstract

fetched live from OpenAlex

Maritime ports are critical nodes in the Canadian resource-based economy that can have significant environmental impacts near coastal communities and marine ecosystems. To address these impacts, Canadian Port Authorities (CPAs) assess their environmental performance using the Green Marine Environmental Program (GMEP). Reliance on this program necessitates its evaluation as an effective initiative to address sustainability in its broader context. An analysis was performed to identify links between United Nations Sustainable Development Goals (UN SDG) targets relevant to the Canadian Port Sector and GMEP performance indicators. Results indicate that there are significant gaps in the GMEP, with only 14 of 36 relevant SDG targets directly linked to the program. Findings suggest either an expansion of the GMEP to incorporate these broader sustainability goals, or the development and inclusion of a new framework for CPAs to bridge gaps between the GMEP and SDG targets to improve sustainability in their maritime port operations.

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)0.034
metaresearch head score (Gemma)0.078
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 score0.124
Threshold uncertainty score0.899

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.078
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0070.004
Scholarly communication0.0060.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.322
Teacher spread0.290 · 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

Citations18
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueSustainabilitySame topicMaritime Ports and LogisticsFrench-language works237,207