MétaCan
Menu
Back to cohort
Record W2906429613 · doi:10.1038/s41598-018-36548-0

Aluminum-target-assisted femtosecond-laser-filament-induced water condensation and snow formation in a cloud chamber

2018· article· en· W2906429613 on OpenAlexfundno aff
Liu Y, Jiansheng Liu, Haiyi Sun, Jingjing Ju, Xinkai Hu, Cheng Wang, Yuxin Leng

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsnot available
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaUniversité Laval
KeywordsFemtosecondProtein filamentCloud chamberCloud condensation nucleiSnowLaserCondensationMaterials scienceCloud computingEnvironmental scienceChemical engineeringOpticsMeteorologyPhysicsComposite materialComputer scienceNuclear physicsAerosol

Abstract

fetched live from OpenAlex

We compare the water condensation and snow formation induced by a femtosecond laser filament with that when the filament is assisted by an aluminum target located at different positions along the filament. We reveal that the laser-filament-induced water condensation and snow formation assisted by the aluminum target are more efficient compared with those obtained without the assistance of the aluminum target. We find that the mass of the snow induced by the laser filament is the largest when the aluminum target is located at the end of the filament, smaller when it is at the middle of the filament, and the smallest at the beginning of the filament. These findings indicate that a higher plasma density and the generation of vortex pairs below the filament are important for enhancing the efficiency and yield of the laser-induced water condensation and precipitation. The higher plasma density provides more cloud condensation nuclei and facilitates the water condensation; vortex pairs below the filament are favourable to the growth of particles up to larger sizes.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicLaser-Matter Interactions and ApplicationsFrench-language works237,207