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Record W4312873568 · doi:10.15376/biores.12.2.hubbe

Deacidification of acidic books and paper by means of non-aqueous dispersions of alkaline particles: A review focusing on completeness of the reaction

2017· review· en· W4312873568 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.

Bibliographic record

VenueBioResources · 2017
Typereview
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsFPInnovations
Fundersnot available
KeywordsAqueous solutionNeutralizationAlkaline hydrolysisHydrolysisCondensationChemistryChemical engineeringPolymer scienceMaterials scienceOrganic chemistryEngineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

Deacidification refers to chemical treatments meant to slow down the acid hydrolysis and embrittlement of books and paper documents that had been printed on acidic paper. From the early 1800s up to about 1990, papermakers used aluminum sulfate, an acidic compound, in most printing papers. Certain deacidification methods use non-aqueous media to distribute alkaline mineral particles such as MgO within the pages of the treated books. Evidence is considered here as to whether or not the proximity of alkaline particles within such documents is sufficient to neutralize the acidic species present. Because much evidence suggests incomplete neutralization, a second focus concerns what to do next in cases where books already have been treated with a non-aqueous dispersion system. Based on the literature, the neutralization of acidic species within such paper can be completed by partial moistening, by high humidity and pressure, by water condensation, as well as by optional treatments to enhance paper strength and a final drying step.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.126
GPT teacher head0.316
Teacher spread0.190 · 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