MétaCan
Menu
Back to cohort
Record W2042459864 · doi:10.1121/1.4786431

Estimation of bolide energy using observed infrasound signals

2005· article· en· W2042459864 on OpenAlexaff
W. N. Edwards, Peter Brown, Douglas O. ReVelle

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsAltitude (triangle)AmplitudePopulationExplosive materialStratosphereRange (aeronautics)Environmental scienceInfrasoundMagnitude (astronomy)Atmospheric sciencesGeologyPhysicsMeteorologyAstrophysicsGeographyOpticsMaterials scienceMathematicsAcoustics

Abstract

fetched live from OpenAlex

The acoustic amplitude-yield relationships including formal errors for a population of large and well-observed (greater than 0.05 kton) bolide events have been investigated. Using various signal measurements as a function of range, these data have been calibrated against optical yield estimates from satellite measurements. A correction for the presence of stratospheric winds has also been applied to the observations and is found to greatly improve the relationship correlation, remaining scatter may be due to variations amongst the fireball population such as differing burst altitudes, greater or lesser amounts of fragmentation or the variability in the magnitude of stratospheric winds, which can during certain times of the year be comparable to or exceed the strength of the winds themselves. Comparison to point source, ground-level nuclear and high explosive airwave data with similar yields shows that observed bolide infrasound is consistently lower in amplitude. This downward shift relative to nuclear and HE data is interpreted as due in part to increased weak nonlinearity during signal propagation from higher altitudes. This is a likely explanation, since mean estimates of the altitude of maximum energy deposition along the bolide trajectory was found to be between 20–30 km altitude for this fireball population.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.562

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicEarthquake Detection and AnalysisFrench-language works237,207