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Record W4230983511 · doi:10.6028/nist.ir.7941

Forensic science laboratories : handbook for facility planning, design, construction, and relocation

2013· report· en· W4230983511 on OpenAlexaff
James Aguilar, Tom Barnes, Joseph Browne, Alison Kennedy, Romeo Miranda, Shannan Williams, Yvette Burney, John C. Byrd, Bonnie Carver, Jim McClaren, Russell McElroy, Adam Denmark, Michael K. Mount, Susan Halla, Lou Hartman, Kenneth Mohr, Deborah Leben, Greg Matheson, Steve Sigel, Jennifer Smither, Melissa Taylor, Aliece Watts

Bibliographic record

Venuenot available
Typereport
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsPositive Living North
Fundersnot available
KeywordsRelocationEngineeringConstruction engineeringEngineering managementSystems engineeringArchitectural engineeringComputer scienceOperating system

Abstract

fetched live from OpenAlex

It is NIST policy to use the International System of Units (metric units) in all its publications.In this report, however, information is presented in U.S. Customary Units (inch-pound), as this is the preferred system of units in the U.S. building industry.

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)0.007
metaresearch head score (Gemma)0.009
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.082
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0050.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0820.120

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 head0.037
GPT teacher head0.280
Teacher spread0.244 · 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
GenreOther

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

Citations4
Published2013
Admission routes1
Has abstractyes

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