MétaCan
Menu
Back to cohort

Evaluation of Dechlorinating Chemicals for Spent Membrane Cleaning Solutions

2016· article· en· W2343702251 on OpenAlex
Jiyun Ko, Wayne J. Parker, Jun Liu

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

VenueWater Environment Research · 2016
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsRegional Municipality of DurhamUniversity of Waterloo
Fundersnot available
KeywordsSodium thiosulfateChemistryThiosulfateChlorineCalciumSodium sulfiteSulfiteInorganic chemistrySodium bisulfiteSodiumNuclear chemistryEnvironmental chemistryOrganic chemistrySulfur

Abstract

fetched live from OpenAlex

The use of dechlorinating chemicals for removal of chlorine from spent membrane cleaning solutions was investigated. Addition of calcium thiosulfate resulted in a decrease in pH at low dosages of calcium thiosulfate, but when higher dosages were used, the pH was not affected. Other dechlorinating agents (sodium bisulfite, sodium sulfite, and ascorbic acid) generated smaller pH declines than calcium thiosulfate. The declines in pH were observed after the dechlorination reaction was effectively complete and pH did not appear to influence the rate of dechlorination. The rate of dechlorination in spent cleaning solution was slower than that observed in clean water. Dechlorination with calcium thiosulfate resulted in the lowest half-life and reaction time. At lower doses (less than 565 mg/L), it was not possible to discriminate between the different dechlorinating agents. The times required for dechlorination were more sensitive to increases in dechlorination chemical dose at lower doses.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.191
GPT teacher head0.375
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