MétaCan
Menu
Back to cohort
Record W1515206636 · doi:10.15376/biores.5.3.1923-1935

Thermal stability of metal-pitch deposits from a spruce thermomechanical pulp by use of a differential scanning calorimeter

2010· article· en· W1515206636 on OpenAlexaff
Zhongguo Dai, Yonghao Ni

Bibliographic record

VenueBioResources · 2010
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDifferential scanning calorimetryValencyThermal stabilityMaterials scienceMetalCalorimeter (particle physics)Cone calorimeterMetal ions in aqueous solutionPulp (tooth)Composite materialMetallurgyChemistryCharOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Pitch-related deposition has been a significant issue in paper mills that produce wood-containing paper grades. A component analysis showed that a mill deposit sample was a mixture of wood resin, fiber, metal cations, and other inorganics. Based on the differential scanning calorimeter (DSC) method, some critical parameters, including pH, metal cations, and their interactions, on the thermal stability of pitch-related deposits were studied. The valency of metal cations determined the ability of capturing pitch the formation of deposits. Trivalent Al3+ or Fe3+ ions had much stronger effects than divalent Ca2+, Mg2+, or Mn2+. It was also found that a higher pH and trivalent Al3+ or Fe3+ increased the thermal stability of deposits formed in colloidal pitch solutions.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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 designBench or experimental
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

Citations8
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueBioResourcesSame topicExtraction and Separation ProcessesFrench-language works237,207