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Record W3207269482

기술자료 : 특허정보를 활용한 습식 이산화탄소 포집 기술동향 분석

2015· article· ko· W3207269482 on OpenAlex
이윤석, 이수진, 이정구, 홍순직

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the Korean Society for Heat Treatment · 2015
Typearticle
Languageko
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasChinaCarbon capture and storage (timeline)Patent analysisBusinessNatural resource economicsPolitical scienceClimate changeData scienceComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

As recognized by all scientific and industrial groups, carbon dioxide (CO2) capture and sequestration (CCS) could play an important role in reducing greenhouse gas emissions. This paper is aimed at identifying evolving technological trends from the objective information of patents related to carbon capture technology by solvent. In this study the patents applied in korea, japan, china, canada, US, EU from 1993 to 2013 were analyzed. The result of patent analysis could be used for RD Revised August 17, 2015; Accepted September 1, 2015)

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.

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)0.001
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 score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.281
Teacher spread0.244 · 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