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Record W2066946439 · doi:10.1504/ijttc.2007.017806

Biosorption technology: starting up an enterprise

2007· article· en· W2066946439 on OpenAlex
Bohumil Volesky, Ghinwa Naja

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

VenueInternational Journal of Technology Transfer and Commercialisation · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiosorptionGeneral partnershipProcess (computing)BusinessCompetition (biology)Industrial organizationProcess managementEngineering managementEngineeringComputer scienceChemistryFinanceEcology

Abstract

fetched live from OpenAlex

Research into biosorption elucidated the principles of this effective process for water decontamination. While it seems that this technology could hardly have any competition, the process has not been applied as yet and several commercialisation attempts have not been successful. As solid capitalisation is required for innovative process ventures, partnership approach is perhaps advisable. While mining companies appear to be excellent 'clients', each is invariably concerned with having its own environmental problems successfully addressed. The 'suppliers' of ion exchange technologies is a handful of huge transnational companies with difficulties in operative decision making. Dynamic consulting companies are in an excellent position to push new technologies into the marketplace. However, they are not known as capital-rich entities. All these aspects make a wide industrial application of the new biosorption process quite a challenge.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.279
Teacher spread0.269 · 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