MétaCan
Menu
Back to cohort
Record W2057023802 · doi:10.7901/2169-3358-2005-1-663

ENERGY AND WORK INPUT IN LABORATORY VESSELS

2005· article· en· W2057023802 on OpenAlexaff
Merv Fingas

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsParticle image velocimetryEnergy (signal processing)TurbulenceWork (physics)MechanicsParticle (ecology)Turbulence kinetic energyParticle tracking velocimetryOpticsPhysicsAcousticsGeology

Abstract

fetched live from OpenAlex

ABSTRACT Turbulent and total energy are known be a very important part of the measurement of oil spill processes. For example, energy is thought to be the single most-important variable in relation to chemical dispersion. Two techniques have been initiated to measure energy. The measurement technique chosen to do this is Particle Image Velocimetry or PIV. In this method, seed particles are put into the fluid and the fluid is illuminated with a laser. The movement of a particle in a given cell is measured as a function of time. This can occur as fast as 30 Hz. Energy can be calculated from at least 2 successive frames. Turbulent energy can be calculated at each point in the image frame. The other method used is the method of using hot wire anemometry. This method can yield data similar to PIV, however requires the intrusion of a probe into the area. The measurements are compared to calculations based on formulations presented in the literature. An important point is that it is shown that a single value does not represent the energy in a vessel (or at sea) because the energy level is not homogeneous throughout the field nor is it simply described. Several of the laboratory vessels have energy fields that are representative of sea conditions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
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 communication0.0000.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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 designNot applicable
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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueInternational Oil Spill Conference ProceedingsSame topicOil Spill Detection and MitigationFrench-language works237,207