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Record W2985097214 · doi:10.1111/ijag.14560

Investigation of the crystallization behavior of laser‐irradiated EXTREME pattern by Raman spectroscopy

2019· article· en· W2985097214 on OpenAlexaff
Megan Driggers, Clara Rivero‐Baleine

Bibliographic record

VenueInternational Journal of Applied Glass Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsLockheed Martin (Canada)
FundersUniversity of Central FloridaMassachusetts Institute of TechnologyDefense Advanced Research Projects AgencyLockheed MartinU.S. Department of Defense
KeywordsMaterials scienceRaman spectroscopyChalcogenideLaserCrystallizationAmorphous solidSpectroscopyIrradiationPhase (matter)OpticsOptoelectronicsAnalytical Chemistry (journal)CrystallographyChemical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract This paper investigates the crystallization behavior of a Ge 2 Sb 2 Se 4 Te 1 (GSST) chalcogenide phase change material film using Raman spectroscopy. The properties of as‐deposited, thermally treated, and laser‐written GSST films are investigated. A single mask exposure laser excitation process was developed using a spatial light modulator with an 800 nm femto‐second laser to generate a direct laser‐written EXTREME pattern. The correlation between the laser‐imprinted EXTREME pattern and the phase change transition from amorphous to crystalline state of the GSST film was characterized using Raman spectroscopy.

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 score0.003

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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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