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Record W2004943236 · doi:10.1063/1.4862814

Semi-automatic laboratory goniospectrometer system for performing multi-angular reflectance and polarization measurements for natural surfaces

2014· article· en· W2004943236 on OpenAlexfundno aff
Zhongqiu Sun, Zhuo Wu, Yunshe Zhao

Bibliographic record

VenueReview of Scientific Instruments · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing in Agriculture
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNortheast Normal UniversityUniversity of Lethbridge
KeywordsOpticsPolarization (electrochemistry)Linear polarizationPolarimetrySnowReflectivityMaterials scienceRemote sensingScatteringPhysicsGeologyLaserChemistry

Abstract

fetched live from OpenAlex

In this paper, the design and operation of the Northeast Normal University Laboratory Goniospectrometer System for performing multi-angular reflected and polarized measurements under controlled illumination conditions is described. A semi-automatic arm, which is carried on a rotated circular ring, enables the acquisition of a large number of measurements of surface Bidirectional Reflectance Factor (BRF) over the full hemisphere. In addition, a set of polarizing optics enables the linear polarization over the spectrum from 350 nm to 2300 nm. Because of the stable measurement condition in the laboratory, the BRF and linear polarization has an average uncertainty of 1% and less than 5% depending on the sample property, respectively. The polarimetric accuracy of the instrument is below 0.01 in the form of the absolute value of degree of linear polarization, which is established by measuring a Spectralon plane. This paper also presents the reflectance and polarization of snow, soil, sand, and ice measured during 2010-2013 in order to illustrate its stability and accuracy. These measurement results are useful to understand the scattering property of natural surfaces on Earth.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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 designBench or experimental
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

Citations58
Published2014
Admission routes1
Has abstractyes

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