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Record W2049182919 · doi:10.1134/s0038094608020020

The extra-atmospheric mass of small meteoroids of the Prairie and Canada bolide camera networks

2008· article· en· W2049182919 on OpenAlexaboutno aff
N. V. Popelenskaya, V. P. Stulov

Bibliographic record

VenueSolar System Research · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsMeteoroidMeteor (satellite)Atmosphere (unit)Environmental scienceBrightnessAtmospheric sciencesMeteorologyGeologyAstronomyPhysics

Abstract

fetched live from OpenAlex

The existing methods for determining the extra-atmospheric mass of meteor bodies from observations of their movement in the atmosphere allow a certain arbitrariness. Active attempts to overcome the discrepancy between the results of calculations based on different approaches often lead to physically incorrect conclusions. A way out is to laboriously accumulate the estimates and computation results and to consistently remove ambiguities. To correctly interpret the observed brightness of a meteor, one should use contemporary methods and the results of physical studies of the emitting gas. In the present work, the extra-atmospheric masses of small meteoroids of the Prairie and Canada bolide camera networks were calculated from the observed braking. It turned out that, in many cases, the conditions of movement of meteor bodies in the atmosphere corresponded to a free molecular airflow about a body. The so-called dynamic mass of the bodies was estimated from the real densities of the meteoroid material, which corresponded to monolithic water ice and stone, and for the proper values of the product of the drag coefficient and shape factor. When producing the trial function for the body trajectories in the “velocity-altitude” variables, we did not allow for fragmentation explicitly, since it is less probable for small meteoroids than for large ones. As before, our estimates differ substantially from the photometric masses published in the corresponding tables.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.926

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 head0.022
GPT teacher head0.233
Teacher spread0.212 · 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 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

Citations5
Published2008
Admission routes1
Has abstractyes

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