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Record W4401177132 · doi:10.36688/imej.7.1-9

Thinking big starting small: identifying considerations for small-scale tidal energy in southwest Nova Scotia

2024· article· en· W4401177132 on OpenAlexaffabout
John Colton, Jayden Alp

Bibliographic record

VenueInternational Marine Energy Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsAcadia University
Fundersnot available
KeywordsNova scotiaNova (rocket)Scale (ratio)Tidal powerOceanographyGeographyEnvironmental scienceGeologyAeronauticsCartographyEngineeringMarine engineering

Abstract

fetched live from OpenAlex

Nova Scotia’s marine renewable energy (MRE) sector can contribute to provincial goals for carbon neutrality. With this, tidal energy has been an interest to industry stakeholders for over two decades, yet general momentum for the industry has shifted. Previous tidal energy projects and recent studies suggest there needs to be a shift in focus towards scalable tidal energy (i.e., small-scale) development in the province. This research explores the considerations of developing tidal energy in Southwest Nova Scotia with an emphasis on small-scale tidal devices. Using a comprehensive literature review and in-depth semi-structured interviews with stakeholders from different sectors of Nova Scotia’s MRE energy industry, key issues and challenges were explored. Results underline four main themes that affect industry potential, which include cost and financing, technology, policy, and energy distribution. These results support a discussion around the value of small-scale technology, regulatory pathways, industry collaboration, and using global best practices to accelerate tidal energy industry goals in Nova Scotia.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score0.998

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 communication0.0010.000
Open science0.0000.002
Research integrity0.0000.000
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 head0.029
GPT teacher head0.247
Teacher spread0.218 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2024
Admission routes2
Has abstractyes

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