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Record W3207545632

Blood Spatter Patterns

2019· preprint· de· W3207545632 on OpenAlexaboutno aff
George Rajna

Bibliographic record

VenueviXra · 2019
Typepreprint
Languagede
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNanotechnologyQuantum dotPhotoluminescencePhotonicsLaserMaterials scienceComputer scienceOptoelectronicsPhysicsOptics
DOInot available

Abstract

fetched live from OpenAlex

Blood spatters are hydrodynamic signatures of violent crimes, often revealing when an event occurred and where the perpetrator and victim were located at the time of the crime. [31] Focused ultrasound from outside the body can safely and reversibly open the blood-brain barrier in patients with Alzheimer's disease, researchers in Canada have shown for the first time. [30] Small vessel vasculitis-inflammation of the small blood vessels-appears as a stain of tiny, red dots covering the skin that, depending on the severity, can evolve into painful pustules or ulcers. [29] Scientists from the University of Freiburg have developed materials systems that are composed of biological components and polymer materials and are capable of perceiving and processing information. [28] Nanotechnology may provide an effective treatment for Parkinson's disease, a team of researchers suggests. [27] Recent research from Kumamoto University in Japan has revealed that polyoxometalates (POMs), typically used for catalysis, electrochemistry, and photochemistry, may also be used in a technique for analyzing quantum dot (QD) photoluminescence (PL) emission mechanisms. [26] Researchers have designed a new type of laser called a quantum dot ring laser that emits red, orange, and green light. [25] The world of nanosensors may be physically small, but the demand is large and growing, with little sign of slowing. [24] In a joint research project, scientists from the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy (MBI), the Technische Universitat Berlin (TU) and the University of Rostock have managed for the first time to image free nanoparticles in a laboratory experiment using a highintensity laser source. [23] For the first time, researchers have built a nanolaser that uses only a single molecular layer, placed on a thin silicon beam, which operates at room temperature. [22] A team of engineers at Caltech has discovered how to use computer-chip manufacturing technologies to create the kind of reflective materials that make safety vests, running shoes, and road signs appear shiny in the dark. [21]

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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