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Record W2029654989 · doi:10.1080/10889370009377698

The circumpolar active layer monitoring (calm) program: Research designs and initial results

2000· article· en· W2029654989 on OpenAlexaboutno aff
Jerry Brown, Kenneth M. Hinkel, Frederick E. Nelson

Bibliographic record

VenuePolar Geography · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostActive layerCircumpolar starClimatologyThermokarstNorthern HemisphereEnvironmental scienceLatitudeAtmospheric sciencesGeologyOceanographyLayer (electronics)

Abstract

fetched live from OpenAlex

The Circumpolar Active Layer Monitoring (CALM) program, designed to observe the response of the active layer and near‐surface permafrost to climate change, currently incorporates more than 100 sites involving 15 investigating countries in both hemispheres. In general, the active layer responds consistently to forcing by air temperature on an interannual basis. The relatively few long‐term data sets available from northern high‐latitude sites demonstrate substantial interannual and interdecadal fluctuations. Increased thaw penetration, thaw subsidence, and development of thermokarst are observed at some sites, indicating degradation of warmer permafrost. During the mid‐ to late‐1990s, sites in Alaska and northwestern Canada experienced maximum thaw depth in 1998 and a minimum in 2000; these values are consistent with the warmest and coolest summers. The CALM network is part of the World Meteorological Organization's (WMO) Global Terrestrial Network for Permafrost (GTN‐P). GTN‐P observations consist of both the active layer measurements and the permafrost thermal state measured in boreholes. The CALM program requires additional multi‐decadal observations. Sites in the Antarctic and elsewhere in the Southern Hemisphere are presently being added to the bipolar network.

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)0.040
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.150
GPT teacher head0.361
Teacher spread0.211 · 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

Citations526
Published2000
Admission routes1
Has abstractyes

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