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Record W3022234692 · doi:10.5006/3520

Corrosion in Caustic Soda in Mineral Processing Operations

2020· article· en· W3022234692 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

VenueCORROSION · 2020
Typearticle
Languageen
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsRichmond Hospital
Fundersnot available
KeywordsSulfuric acidHydrometallurgyCorrosionLeaching (pedology)Mineral processingSodium hydroxideMetallurgyHydroxideSmeltingAlkaline batteryMaterials scienceMaterials processingChemistryInorganic chemistryEnvironmental scienceEngineeringElectrolyte

Abstract

fetched live from OpenAlex

Because of the high cost of energy, hydrometallurgy is frequently preferred to smelting to extract metals from ores. Many of these processes involve leaching of the metal with an acid, often sulfuric acid. However, there are some processes that use alkaline leachates, such as sodium hydroxide. Hot alkaline solutions present somewhat different corrosion problems to acidic ones and this paper presents data on the corrosion performance of metals and polymers in hot alkaline solutions. Some case histories from the mineral processing industry are used to demonstrate the importance of correct material selection in these corrosive 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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