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Record W2543031636 · doi:10.1109/iqec.2000.908180

Using femtosecond technology to control molecular dynamics in strong laser fields

2000· article· en· W2543031636 on OpenAlexaff
Mikhail Ivanov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAtom opticsPhysicsAccelerationLaserOptical physicsDegrees of freedom (physics and chemistry)FemtosecondQuantum opticsCoherent controlOptical tweezersAtom (system on chip)Laser coolingPhysical opticsField (mathematics)OpticsClassical mechanicsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

Summary form only given. The desire to control the dynamics of quantum systems is both a driving force and a unifying theme in many areas of modern laser-matter interaction physics. For example, optical manipulation of cold atoms (atom optics) focuses on controlling the translational degrees of freedom of an atom, while various coherent control techniques attempt to use laser fields to manipulate the internal dynamics of atomic and molecular systems. I will describe approaches in strong-field molecular optics. Molecular optics shares with atom optics the aim of controlling position, velocity and acceleration. It also aims to control intrinsically molecular properties such as rotations, from alignment to angular acceleration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.282
Teacher spread0.275 · 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 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
Published2000
Admission routes1
Has abstractyes

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