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Record W7097044572

Automatic Generation of Time-lapse Animations

2003· article· en· W7097044572 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsnot available
Fundersnot available
KeywordsAnimationSoftwareJPEGComputer animationPerlOpen sourceOpen source softwareNon-linear editing system
DOInot available

Abstract

fetched live from OpenAlex

A simple system for the automatic generation of time-lapse animations has been created at the Aerodynamics Laboratory of the Institute for Aerospace Research, National Research Council of Canada (NRC). The system consists of two small Perl programs and a static web page, and relies heavily on two open source software packages: LWP::Simple, and ImageMagick. The first program, tlget.pl, runs for several hours each weekday and is responsible for the periodic acquisition (and storage) of images from a webbased camera. The second program, tlmpeg.pl, is responsible for the conversion of groups of JPEG format images into MPEG format animations. Typically, tlmpeg.pl is executed each weekday night, and generates three animation files, one from the past day's images, one from the past week's images, and one from the past month's images. A static web page has been created to display the image most recently acquired by tlget.pl, and to provide links to each of the three animation files most recently generated by tlmpeg.pl.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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.

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

Citations0
Published2003
Admission routes1
Has abstractyes

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