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Record W2053176824 · doi:10.1165/rcmb.2014-0469ma

Automated High-Performance Analysis of Lung Morphometry

2015· article· en· W2053176824 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Respiratory Cell and Molecular Biology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersCanadian Institutes of Health Research
KeywordsLungComputer scienceComputational biologyMedicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Automation of lung morphometric analysis is an asset in the study of lung pathophysiology because it is an assurance of robustness, reproducibility, and rapidity. The novel automated morphometric approach presented here meets these criteria. This new method collects multiple parameters, allowing quantitative elucidation of the pathophysiology of the developing and mature lungs. The automated morphometric analysis is reliable and allows the analysis of a greater proportion of each lung together with a higher number of samples and superior reproducibility than manual analysis. The use of this method revealed that treatment with 80% oxygen and lung development presented an opposite effect on most of the analyzed parameters. In conclusion, this novel approach allowed the collection of new fundamental morphometric data on lung development and a deeper comprehension of the effect of hyperoxia.

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.

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 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 score0.599
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.289
Teacher spread0.279 · 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