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Record W331712219 · doi:10.1007/s10230-015-0337-5

Multiple Modeling Approach for the Aquatic Effects Assessment of a Proposed Northern Diamond Mine Development

2015· article· en· W331712219 on OpenAlexaffabout
Jerry Vandenberg, M. Herrell, John W. Faithful, Alison Snow, J. Lacrampe, Christine Bieber, Shadi Dayyani, V. Chisholm

Bibliographic record

VenueMine Water and the Environment · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsDe Beers (Canada)Golder Associates (Canada)
Fundersnot available
KeywordsHydrogeologyEnvironmental scienceKey (lock)Component (thermodynamics)GroundwaterHydrology (agriculture)Computer scienceGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Eight water models were used to assess potential aquatic environmental effects of the proposed Gahcho Kué diamond mine on groundwater and surface water flow and quality in the Northwest Territories, Canada. This sequence of models was required to cover different spatial and temporal domains, as well as specific physico-chemical processes that could not be simulated by a single model. Where their domains overlapped, the models were interlinked. Feedback mechanisms amongst models were addressed through iterative simulations of linked models. The models were used to test and refine mitigation plans, and in the development of aquatic component monitoring programs. Key findings generated by each model are presented here as testable hypotheses that can be evaluated after the mine is operational. This paper therefore offers a record of assumptions and predictions that can be used as a basis for post-validation.

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)0.002
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: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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