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Record W2779282010 · doi:10.22323/1.301.0754

TAIGA-HiSCORE detection of the CATS-Lidar on the ISS as fast moving point source

2017· article· en· W2779282010 on OpenAlexfundno aff
R. Wischnewski, А. Порелли, A. Garmash, И. И. Астапов, P. A. Bezyazeekov, V. Boreyko, А. Бородин, Martin Brueckner, Н. Буднев, A. Chiavassa, A. N. Dyachok, Oleg Fedorov, A. Gafarov, N. Gorbunov, E. Gorbovskoy, V. Grebenyuk, О. Гресс, Т. И. Гресс, О. Гришин, A. A. Grinyuk, D. Horns, Ivanova La, Н. Н. Калмыков, Y. Kazarina, В. В. Киндин, Pavel Kirilenko, S. Kiryuhin, Р. П. Кокоулин, К. Г. Компаниец, Е. Е. Коростелева, Vladimir V Kozhin, Е. А. Кравченко, М. Куннас, L. A. Kuzmichev, Yu. Lemeshev, Vladimir Lenok, Б. Лубсандоржиев, V. Lipunov, Н. Лубсандоржиев, R. Mirgazov, Razmik Mirzoyan, Р. Монхоев, R. Nachtigall, E. Osipova, А. Пахоруков, M. I. Panasyuk, L. Pankov, А. А. Петрухин, V. Poleschuk, E. Popescu, Elena Popova, Е. Постников, В. В. Просин, В. С. Птускин, E.V. Rjabov, Г. И. Рубцов, А. Пушнин, Yaroslav Sagan, B. Sabirov, В. Самолига, Yu. Semeney, А. Силаев, A. Silaev, Andrey Sidorenkov, Aleksandr Vasilevich Skurihin, V. Slunecka, A. Sokolov, C. Spiering, G. Spengler, L. Sveshnikova, В. А. Таболенко, B. Tarashansky, A. Tkachenko, Л. Ткачев, M. Tluczykont, А. Загородников, Д. Журов, В. Л. Зурбанов, I. V. Yashin

Bibliographic record

VenueProceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsnot available
FundersRussian Science FoundationRussian Foundation for Basic ResearchDeutsche ForschungsgemeinschaftEuropean CommissionMcGill University
KeywordsLidarRemote sensingLaserCalibrationOpticsPhysicsCherenkov radiationEnvironmental scienceDetectorGeology

Abstract

fetched live from OpenAlex

We report the first ground-based observation of the CATS-LIDAR onboard the ISS by the TAIGA-HiSCORE gamma-ray facility, and the MASTER-Tunka Robotic telescope. HiSCORE detects unscattered laser light directly from the ISS, at up to km-scale distance from the laser beam spot on ground. The ISS-LIDAR turns out to be a unique calibration tool, in particular to verify the absolute astronomical pointing of HiSCORE. We detected the LIDAR for 11 ISS-passages; among these were observations of forward scattering of the laser beam in dense clouds, which might carry information complementary to the LIDAR technique. We expect other air Cherenkov installations like IACTs to benefit from this light source.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.001
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 head0.015
GPT teacher head0.236
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2017
Admission routes1
Has abstractyes

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