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Record W2168212007 · doi:10.1002/2014gl062773

China experiencing the recent warming hiatus

2015· article· en· W2168212007 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsHiatusClimatologyMean radiant temperatureEnvironmental scienceContext (archaeology)DrynessGlobal warmingClimate changeAtmospheric sciencesGeographyGeologyOceanographyBiology

Abstract

fetched live from OpenAlex

Abstract Based on the homogenized data set, we analyze changes in mean temperature and some extreme temperature indices over China since 1961 and especially during the recent warming hiatus period (1998–2012) in a global average context. The result shows that the decrease of annual mean maximum has contributed most to the decreases in overall mean temperature and in diurnal temperature range (DTR) during the warming hiatus period. In most parts of China except the southwest, the summer mean maximum temperature ( T xS ) shows the largest increase, while the winter mean minimum temperature ( T nW ) indicates slight cooling trends. These changes have augmented the seasonal cycle and increased the likelihood of extreme warm and cold events. Further analyses reveal that the increases in T xS are significantly correlated with concurrent increases in solar radiation. In southwest China, the annual mean temperature, T xS , T nW , and DTR increased during 1998–2012, possibly related to increased dryness in this region during the hiatus period.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.340
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