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Record W2058382947 · doi:10.1364/boe.6.000032

Femtosecond laser bone ablation with a high repetition rate fiber laser source

2014· article· en· W2058382947 on OpenAlexaff
Luke J. Mortensen, Clemens Alt, Raphaël Turcotte, Marissa Masek, Tzu‐Ming Liu, Daniel Côté, Chris Xu, Giuseppe Intini, Charles P. Lin

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversité Laval
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsMaterials scienceAblationLaserLaser ablationFemtosecondBiomedical engineeringOpticsMedicine

Abstract

fetched live from OpenAlex

Femtosecond laser pulses can be used to perform very precise cutting of material, including biological samples from subcellular organelles to large areas of bone, through plasma-mediated ablation. The use of a kilohertz regenerative amplifier is usually needed to obtain the pulse energy required for ablation. This work investigates a 5 megahertz compact fiber laser for near-video rate imaging and ablation in bone. After optimization of ablation efficiency and reduction in autofluorescence, the system is demonstrated for the in vivo study of bone regeneration. Image-guided creation of a bone defect and longitudinal evaluation of cellular injury response in the defect provides insight into the bone regeneration process.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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