MétaCan
Menu
← Back to cohort
Record W2956286430 · doi:10.22215/etd/2018-12686

Metal Transport near a Tailings Facility in the Alberta Oil Sands

2018· dissertation· en· W2956286430 on OpenAlexafffundabout
Stephanie Roussel

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsCarleton University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsOil sandsTailingsEnvironmental scienceWetlandSorptionEnvironmental chemistryTrace metalUnconventional oilEnvironmental engineeringWaste managementMining engineeringGeologyFossil fuelMetalChemistryAsphaltEcologyEngineeringAdsorptionArchaeologyGeography

Abstract

fetched live from OpenAlex

The main concern that surrounding the large-scale, industrial oil sands operations in the Alberta Oil Sands is the potential for oil sands process-affected water to leak from tailings facilities into the surrounding environments.Many parameters control trace metal migration as OSPW enters wetland environments that comprise approximately 30% of Northern Alberta, including pH, redox potential, temperature, organic matter, inorganic water chemistry, and hydrology.This study aims to quantify the control organometallic complexes exert on the mobility of trace metal loads where tailings facilities are adjacent to wetland environments.Geochemical modeling indicated that humic substances are the primary sorption phase, and likely dominate the chemical behavior of many metals, however, organometallic complexes are not currently taken into account in environmental monitoring programs.A refined understanding of the environmental and geochemical processes operating within this system is required to determine the potential risk OSPW leakage represents to these ecosystems.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.232
Teacher spread0.219 · 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 designNot applicable
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 routes3
Has abstractyes

Explore more

Same topicGeochemistry and Geologic Mapping→French-language works237,207→