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Record W2944786131 · doi:10.35298/pkc.2018.12

Introduction to Traditional Knowledge studies in support of geoscience tools for assessment of metal mining in Northern Canada

2019· article· en· W2944786131 on OpenAlexvenueaboutno aff
Jennifer Galloway, R. Timothy Patterson

Bibliographic record

VenuePolar Knowledge Aqhaliat Report · 2019
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsEarth scienceData scienceMining engineeringComputer scienceEngineeringGeology

Abstract

fetched live from OpenAlex

This report describes a three-year project (2015–2018) to improve understanding of past climate. It studies the way metal(loids) have responded to different climate conditions in the past. Lake sediments and peatlands are known as paleoecological archives. In other words, they contain fossils that help understand the past ecology of plants and animals in the area. The study looked at historical climate conditions shown by the chemistry and fossil content of these sediments and peatlands. It also examined Inuit Qaujimajatuqangit (traditional knowledge). This may result in more accurate predictions of how metal(loids) respond to current and forecasted climate change. The Yellowknife and Courageous Lake areas of the mineral-rich Slave Geological Province have gold mining histories. These areas were studied to see how metal(loids) mobilized by past mining activity may move through the environment. The project partners—the North Slave Métis Alliance, the Yellowknives Dene First Nation, the Tłįchǫ Research and Training Institute, and Hadlari Consulting Ltd.—conducted Traditional Knowledge and Inuit Qaujimajatuqangit studies. Information from these studies will be used together with data collected by western scientific techniques. This will provide a better understanding of the transport and fate of metal(loids) under a changing climate.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.326
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2019
Admission routes2
Has abstractno

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