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Record W2900765091

Canadian Coast Guard initiatives

2018· article· en· W2900765091 on OpenAlexaboutno aff
T. MCCANN

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCoast guardGeographyBusinessPolitical scienceEnvironmental protection
DOInot available

Abstract

fetched live from OpenAlex

As part of the Oceans Protection Plan the Canadian Coast Guard will be undertaking several initiatives focused on continued improvement in our preparedness and response to marine emergencies. One aspect will see the establishment of new search and rescue lifeboat stations and a new CCG logistics depot in Port Hardy, BC that will include environmental response staff and equipment. Investments will be made in modernizing Coast Guard’s Environmental Response equipment including mobile command posts and communications equipment. Additionally, the Coast Guard will enhance the 24/7 emergency response capacity within the existing Regional Operations Centers supporting the Coast Guard’s capacity to better plan and coordinate effective response during an incident. Through the Oceans Protection Plan, the Government of Canada will increase its emergency towing capacity by: installing towing equipment on major Canadian Coast Guard vessels, engaging partners to complete a needs analysis study on emergency towing requirements on the west coast and leading to the leasing of offshore vessels capable of towing large ships in distress. Further, the Government of Canada will partner with Indigenous Nations through Indigenous Community Response Teams, Indigenous Coast Guard Auxiliary, Places of Refuge and Regional Response Planning This presentation will provide an overview of Canadian Coast Guard initiatives such as new lifeboat stations, enhanced 24/7 emergency response capacity, increasing emergency towing, and Indigenous Community Response Teams and the Canadian Coast Guard Auxiliary.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.263
Teacher spread0.240 · 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 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
Published2018
Admission routes1
Has abstractyes

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