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Record W2105709731 · doi:10.1175/jcli-d-11-00003.1

Surface Water Vapor Pressure and Temperature Trends in North America during 1948–2010

2012· article· en· W2105709731 on OpenAlexaffabout
Victor Isaac, W. A. van Wijngaarden

Bibliographic record

VenueJournal of Climate · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsYork University
Fundersnot available
KeywordsRelative humidityClimatologyEnvironmental scienceArcticHumidityWater vaporSpring (device)Atmospheric sciencesVapour pressure of waterGeographyMeteorologyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Over one-quarter billion hourly values of temperature and relative humidity observed at 309 stations located across North America during 1948–2010 were studied. The water vapor pressure was determined and seasonal averages were computed. Data were first examined for inhomogeneities using a statistical test to determine whether the data were fit better to a straight line or a straight line plus an abrupt step, which may arise from changes in instruments and/or procedure. Trends were then found for data not having discontinuities. Statistically significant warming trends affecting the Midwestern United States, Canadian prairies, and the western Arctic are evident in winter and to a lesser extent in spring while statistically significant increases in water vapor pressure occur primarily in summer for some stations in the eastern half of the United States. The temperature (water vapor pressure) trends averaged over all stations were 0.30 (0.07), 0.24 (0.06), 0.13 (0.11), 0.11 (0.07) °C decade−1 (hPa decade−1) in the winter, spring, summer, and autumn seasons, respectively. The averages of these seasonal trends are 0.20°C decade−1 and 0.07 hPa decade−1, which correspond to a specific humidity increase of 0.04 g kg−1 decade−1 and a relative humidity reduction of 0.5% decade−1.

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 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)0.000
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.234
Teacher spread0.223 · 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 teacher head, not a consensus.

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

Citations51
Published2012
Admission routes2
Has abstractyes

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