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Record W2137670521 · doi:10.51847/obt7uxx

10.51847/Obt7UXx

2000· article· en· W2137670521 on OpenAlexvenueno aff
P. Nirguna Babu, Prakash Binnal, Dhananjay Kumar

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
Fundersnot available
KeywordsBiosorptionSorptionSpirulina (dietary supplement)Fourier transform infrared spectroscopyAdsorptionDesorptionChemistryPolyurethaneBiomass (ecology)Packed bedNuclear chemistryChromatographyChemical engineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

In the present study, the non living biomass of cyanobacteria Spirulina platensis was used for biosorption of Zn+2 in column mode. Polyurethane foam (PUF) cubes were used for immobilizing the biosorbent. A maximum biomass loading of 0.2 g dry S. platensis /(g of PUF cubes) could be achieved. The effect of parameters (such as pH of feed solution, flow rate of feed solution to column, bed height and initial concentration of metal ion in feed solution )on uptake capacity of biosorbent was studied.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.068
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.9320.939

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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2000
Admission routes1
Has abstractyes

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