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Record W4392576664 · doi:10.15094/00016228

The Second Deep Ice Coring at Dome Fuji, Antarctica.

2020· article· en· W4392576664 on OpenAlexaff
Hideaki Motoyama, Yoshio Tanaka, Morihiro Miyahara, Yoshiyuki Fujii, Teruo Furukawa, Keisuke Suzuki, Makoto Igarashi, Shuji Fujta, Genta Watanabe, Yuanscheng Li, Kotaro Fukui, Takao Kameda, Kokichi Kamiyama, Nobuhiko Azuma, Okitsugu Watanabe, Yukio Ozawa, Yasushi Yoshise

Bibliographic record

VenueInstitutional Repository National Institute of Polar Research (National Institute of Polar Research (Japan)) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsCoringDome (geology)GeologyIce coreOceanographyPhysical geographyPaleontologyGeographyEngineeringDrillingMechanical engineering

Abstract

fetched live from OpenAlex

The second deep ice coring project was carried out at Dome Fuji, Antarctica. Following the pilot hole drilling in 2001, deep ice core drilling was conducted for four years from the 2003/2004 austral summer season, reaching a depth of 3035.22 m in January 2007. The drilling was performed only in the summer season. Therefore, many improvements were made to the problems of the first deep ice core drilling system to enable efficient drilling. In particular, the core length that can be obtained at one time was increased from 2.3m to 3.84 m, and the chip storage efficiency was enhanced. In this report, the outline of the drilling system, the method of drilling, the progress of drilling operation, and various troubles were reported. Also, future issues are indicated.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.111
GPT teacher head0.347
Teacher spread0.236 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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