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Record W6983536438

Modelling algal biological polishing / submitted to Dave Koren CANMET

2018· report· en· W6983536438 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2018
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsUranium mineBioaccumulationPolishingUranium miningRadioactive wasteUranium oreStage (stratigraphy)
DOInot available

Abstract

fetched live from OpenAlex

In this report a proposal to model two biological processes currently utilized by Boojum Research to remove contaminants from wastewater is forwarded. The first model is for heavy metal bioaccumulation by nonrhizoidal, perphytic algae. This model is based on field data collected by Boojum Research from a decommissioned heavy metal mine at Buchans, Newfoundland. The second model is for radionuclide bioaccumulation by characean algae. This model is based on field data from a decommissioned uranium mine at Rabbit Lake, Saskatchewan. Both models are at an early stage of development, and require further work. The final object is to have biological polishing models which can be used as an aid in planning, design, and management of a decommissioned mine site.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.050
GPT teacher head0.247
Teacher spread0.198 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2018
Admission routes1
Has abstractyes

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