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Record W2751207572

SPATIOTEMPORAL VARIABILITY OF HUMIDITY ACROSS THE CONTIGUOUS UNITED STATES AND SOUTHERN CANADA USING REGIONAL NETWORKS

2017· article· en· W2751207572 on OpenAlexaboutno aff
Ruben John Behnke

Bibliographic record

VenueThe Mathematics Enthusiast · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyClimatologyHumidityEnvironmental scienceMeteorologyPhysical geographyGeology
DOInot available

Abstract

fetched live from OpenAlex

The objective of this dissertation was to show that there is now enough observed humidity data available so that estimates of humidity, along with their necessary assumptions, can be replaced by measured humidity data. The range of applications that depend on humidity data is huge, ranging from water use efficiency of plants and plant stress to human health and agricultural practices. Biases due to the use of estimated humidity can be expected to have short and long impacts, decreasing the accuracy and precision of these, and many other, applications. Data from local, regional, and national observation networks was gathered, and custom quality control routines were written to remove bad data points from over 45000 stations, leaving 12533 usable stations. While still not at the same number of observations as temperature or precipitation, this number is nearly ten times as high as two decades ago. The work I performed consists of three major components, corresponding to the three main chapters of this dissertation. In chapter one, I describe data sources and quality control methods, along with some basic statistics of humidity, describing which geographic variables often used to predict temperature and precipitation can be used to do the same for humidity. Chapter two defines specific diurnal patterns (or “types”) of dew point across the United States, including their attributes, causes, and potential influences. Chapter three analyzes biases in evapotranspiration, heat indices, and relative humidity levels that are a direct result of using estimated humidity data. Chapter four discusses contributions this work makes to the scientific community, and potential further research to build on what is presented here. While it may seem that the science of humidity should be well beyond data gathering and bias analysis, the fact remains that humidity is still very commonly estimated through the use of minimum temperature, and diurnal changes in dew point are often ignored. My hope is that this work makes a dent in often deeply entrenched practices, leading others to make the effort to incorporate measured humidity data into their work and study.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

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

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 head0.035
GPT teacher head0.246
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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