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Record W2070604892 · doi:10.1002/cjce.20463

Decreasing the peroxide decomposition in the magnesia slurry

2011· article· en· W2070604892 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Oxide Properties and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsManganeseChemistryPeroxideMagnesiumDecompositionSlurryNuclear chemistrySodium hydroxideHydrogen peroxideInorganic chemistryMineralogyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The peroxide bleaching process for mechanical pulps using weak alkali sources such as magnesium hydroxide (Mg(OH) 2 ), received much attention recently. The magnesia slurry (containing 61.2% Mg(OH) 2 ) can be used for this purpose. In this paper, we studied the manganese‐induced peroxide decomposition in commercial magnesium hydroxide slurry (magnesia). The results showed that peroxide decomposition occurs under the conditions of magnesia based bleaching process. This is due to impurities, particularly manganese in the magnesia slurry. Similar to the conventional sodium hydroxide based process, sodium silicate can effectively decrease the manganese‐induced peroxide decomposition. However, the amount of silicate required is significantly less. It was found that diethylenetriaminepentaacetic acid (DTPA) or its sodium salt, can also be an effective stabiliser in the system. The chemistry of the manganese‐induced peroxide decomposition was discussed.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.200
Teacher spread0.183 · 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