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Record W2891791798 · doi:10.1386/public.29.57.163_1

Ice as a Counter-Archive: Permafrost, Archival Melt and Climate Futures

2018· article· en· W2891791798 on OpenAlexaffabout
Sabrina Perić

Bibliographic record

VenuePublic · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPermafrostEarth scienceArcticClimate changePhysical geographyGeologyAstrobiologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract In the history of Canadian Arctic colonization, ice and permafrost have been understood primarily as an engineering problem. During the Cold War however, understanding of permafrost and its possibilities changed. Microbiologists and geoscientists did not see permafrost as a hindrance, but rather for understanding the past. As permafrost freezes, organics, air and water become trapped, and as the permafrost grows thicker, so too the earliest trapped matter is buried deeper. Through chemical and genomic analysis, permafrosttoday can reveal details about the past. For scientists today, permafrost has collected, ordered and preserved a lost world of climate environments. This paper examines how political imperatives, petroleum industry and government scientists worked together in the 20th century Canadian North to construct permafrost as an archive of the past. It also posits that these icy (and now rapidly melting) archives should play a critical role in our global future.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0130.013
Scholarly communication0.0100.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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 designTheoretical or conceptual
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 routes2
Has abstractyes

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