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Record W7145072846

雪氷コア解析に基づく北部北太平洋の数十年周期気候復元

2007· article· ja· W7145072846 on OpenAlexaboutno aff
Takayuki Shiraiwa

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2007
Typearticle
Languageja
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierIce coreVolcanoIce capsGlobal warmingPacific decadal oscillationClimate changeAntarctic sea ice
DOInot available

Abstract

fetched live from OpenAlex

Three indepewndent ice cores were recently drilled at Ushkovsky Volcano in Kamchatka, Russia, Mount Wrangell in Alaska, and Mount Logan King Col in Canada. Detailed analyses of the temperature and density profiles of the three cores suggest that the three sampled glaciers are cold glaciers and therefore store reliable paleo-climate information that spans the past several hundreds to a thousand years. A negative relation was found for the net accumulation time-series reconstructed for the past 170 years from the ice cores recoverd from Ushkovsky Volcano and Mount Logan. As the oscillations in the net accumulation rate and the average annual δ^18O reconstructed from the Ushkovsky ice core appear to be closely correlated with the so-called Pacific Decadal Oscillation (PDO) Index, it was suggested that the mass balances of the glaciers on both sides of the northern North Pacific were affected not only by a global warming trend since the Little Ice Age but also by inter-decadal climate variability that had occured dominantly over the North Pacific.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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