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Record W2791177608 · doi:10.1017/s1551929500058491

Penetration Rates of Formaldehyde

2002· article· en· W2791177608 on OpenAlexaff
Bryan R. Hewlett

Bibliographic record

VenueMicroscopy Today · 2002
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsFormaldehydePenetration (warfare)Penetration rateChemistryMaterials scienceMathematicsOrganic chemistryGeologyGeotechnical engineeringOperations research

Abstract

fetched live from OpenAlex

Abstract Formaldehyde is one of the most rapidly penetrating fixatives used. Unfortunately, it is one of the slowest to fix tissue. This paradox was finally explained by Burnett in 1982. An fine description of the properties of formaldehyde may be found in John Kiernan's book. The penetration rate of formaldehyde in mm/hr is a variable thing. It depends on how the data is obtained. It may also vary slightly depending on tissue type. The penetration rate of formaldehyde fixatives has been extensively studied, often with conflicting results. The penetration of non-coagulating fixatives is difficult to measure.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.004

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 head0.028
GPT teacher head0.320
Teacher spread0.293 · 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 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

Citations16
Published2002
Admission routes1
Has abstractyes

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