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Record W2104158953 · doi:10.1136/thoraxjnl-2014-206188

Location, location, location: studying anatomically comparable airways is highly relevant to understanding COPD

2014· letter· en· W2104158953 on OpenAlexaff
Benjamin M. Smith, Eric A. Hoffman, Stephen I. Rennard, R. Graham Barr

Bibliographic record

VenueThorax · 2014
Typeletter
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsColumbia CollegeMcGill University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsSampling (signal processing)MedicineAirwayTracheobronchomalaciaComputer scienceSurgeryComputer vision

Abstract

fetched live from OpenAlex

We have read with interest Nakano and colleague's thoughtful comments1 on Smith et al 2 and are pleased to offer the following observations. We believe that a key strength of our paper is that it defines a rigorous sampling strategy to compare airways from matched hierarchical positions within the tracheobronchial tree with control for the known hierarchical gradient in airway dimensions.3 Nakano et al are correct to point out that hierarchical sampling by generation number results in grouping of airways from multiple anatomic locations (eg, segmental and lobar airways); conversely, hierarchical sampling by anatomic location results in grouping of airways from multiple generations.4 It is for this reason that we reported both sampling approaches (tables 2 …

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.046
GPT teacher head0.260
Teacher spread0.214 · 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 designNot applicable
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

Citations3
Published2014
Admission routes1
Has abstractyes

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