MétaCan
Menu
Back to cohort
Record W7027921278

The development of offshore wind energy in Denmark : lessons for Nova Scotia, Canada

2024· dissertation· en· W7027921278 on OpenAlexaboutno aff

Bibliographic record

VenueSkemman · 2024
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodArticular cartilage damageProteogenomicsHyporeflexiaFusible alloy
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates the development of the offshore wind energy sector in Denmark, to make policy recommendations for Nova Scotia, Canada, focusing on spatial, financial, environmental, and policy dimensions. Utilizing content analysis, 73 sources related to the Danish offshore wind energy sector were analyzed to extract relevant keywords and concepts. The study identifies 16 key lessons for advancing offshore wind energy in Nova Scotia. In the spatial cluster, stakeholder engagement, infrastructure development, and cautious scaling are crucial. Financially, mechanisms like feed-in tariffs, debt financing, and green certificates foster growth, while public funds reduce risks. Environmentally, impact studies and biodiversity opportunities are key for sustainable integration. Policy-wise, clear national policies, public-private partnerships, and competitive tendering drive effective sector development. This research contributes to the field by offering comprehensive insights into the success of Denmark’s sustainable energy transition and proposing recommendations for future policy and practice in Nova Scotia, or other regions anticipating the opportunity to harness their offshore wind energy resources.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.996

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 science0.0010.000
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSkemmanSame topicSocial Acceptance of Renewable EnergyFrench-language works237,207