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Record W2361499635

Study on mercury speciation during the course of baking waste alkaline zinc manganese batteries

2006· article· en· W2361499635 on OpenAlex
Huidong Lin

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueActa Scientiae Circumstantiae · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)ZincManganeseChemistryEnvironmental chemistryCombustionPollutionAlkaline batteryWaste managementMetallurgyMaterials scienceElectrode
DOInot available

Abstract

fetched live from OpenAlex

Pollution of mercury in the environment was serious issue and mercury from batteries was attentioned. The work chose waste alkaline zinc manganese batteries and baking experiment was processed in a tubular furnace. According to the analytical method of mercury in coal Combustion (Ontario-Hydro), the experiment studied the speciation and distribution of mercury during baking. The results indicated that the removal ratio of mercury was 100%, and the concentration of total mercury in the end gas was about 186.41 mg·m -3 ~194.86 mg·m -3 .In the gaseous mercury, the content of Hg0 was about 82.88%~86.64% and Hg 2+ was about 6.02%~6.29%,which showed that the majority of gaseous mercury went into end gas by Hg0, So we could reclaim mercury from waste alkaline zinc manganese batteries by condensing end gas directly.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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