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Record W2954857079 · doi:10.1109/cieec.2018.8745721

Compatibility Assessment of Transnational Grid Interconnection between the United States and Canada

2018· article· en· W2954857079 on OpenAlexaboutno aff
Xiaoxi Lv, Pei Zhang, Yao Lu, Jiateng Li, Yan Zhang

Bibliographic record

Venue2018 IEEE 2nd International Electrical and Energy Conference (CIEEC) · 2018
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCompatibility (geochemistry)BackupInterconnectionGridComputer scienceReliability engineeringPoliticsEnvironmental economicsDistributed computingOperations researchEngineeringPolitical scienceTelecommunicationsEconomicsDatabase

Abstract

fetched live from OpenAlex

Transnational grid interconnections are conducive to promoting optimal allocation of resource, reducing generation costs, balancing system load, and reducing system backup capacity. It is necessary to assess compatibility between collaborating countries in order to evaluate feasibility of transnational grid interconnections. Our previous research investigated compatibility assessment methodology in terms of political, social, economic and environmental aspects. This paper performs a case study using the compatibility assessment between the United States and Canada. The evaluation result indicates that the two countries are suitable for grid interconnections. The actual situation of the interconnection between the United States and Canada confirms that the evaluation result is reasonable. Therefore, the methodology promoted previously can be used in compatibility assessment of transnational grid interconnections.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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