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Record W6931392559 · doi:10.5281/zenodo.7879600

The time-in-daylight land-surface parameter

2023· article· en· W6931392559 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and treatment of tuberculosis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPosition (finance)AzimuthRange (aeronautics)Shadow (psychology)HorizonRadiationEstimation theorySurface (topology)

Abstract

fetched live from OpenAlex

Time-in-Daylight (TiD) estimates the portion of total daylight over a time span that a location experiences direct radiation. This paper describes a method for estimating TiD using horizon angle maps derived in a range of azimuths and information about the sun’s position during the time span. TiD is evaluated as a potential land-surface parameter (LSP) for relief mapping and solar radiation modelling applications. The use of horizon angle to map shadow areas in calculating TiD makes this LSP conceptually similar to both openness and sky-view factor (SVF). However, TiD differs most significantly in the pairing of horizon angle maps with a dynamic model of sun position. The findings showed that TiD is well suited to applications in relief visualization, particularly with digital surface models (DSMs) in urban areas. The ability to estimate TiD with specific date/time ranges also makes it better suited for solar radiation modelling applications than either openness or SVF.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.039

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 head0.031
GPT teacher head0.267
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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