MétaCan
Menu
Back to cohort
Record W2981951096 · doi:10.4095/213271

Indicator mineral content and geochemistry of till around the Peddie kimberlite, Lake Timiskaming, Ontario

2002· report· en· W2981951096 on OpenAlexaffabout
M B McClenaghan, B A Kjarsgaard, I M Kjarsgaard

Bibliographic record

Venuenot available
Typereport
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsKimberliteGeologyGeochemistryIlmenitePyropeOlivineMantle (geology)

Abstract

fetched live from OpenAlex

This report presents results of till sampling in the vicinity of the Peddie kimberlite, Bucke Township, in the Lake Timiskaming kimberlite cluster of northeastern Ontario. Thirty till samples were collected (mainly south) of the Peddie kimberlite to document the nature of glacial dispersal from the kimberlite, using both indicator minerals and till geochemistry. Most of the kimberlite indicator minerals in till were found in the finest of the three size fractions (0.25 to 0.5, 0.5 to 1.0 and 1.0 to 2.0 mm) of heavy mineral concentrates examined. Till in the vicinity of the Peddie kimberlite contains a distinctive kimberlitic geochemical signature defined by Ni, Cr, Nb, and Ta, which is most apparent in the coarse to very coarse sand (0.5 to 2.0 mm) and the silt+clay (<0.063 mm) fractions. Two dispersal trains in till were detected near the Peddie kimberlite. One short train immediately down-ice of, and derived from, the Peddie kimberlite is defined by the minerals olivine and Mg-ilmenite, with elevated concentrations of Ni, Cr and Nb. A second dispersal train 800 m southwest of the Peddie kimberlite and trending southeast, is defined by abundant Mg-ilmenite and Cr-pyrope, and elevated concentrations of Nb, and Ta. This second dispersal train is likely derived from the Gravel kimberlite 4 km up-ice (northwest) and/or an unknown kimberlite.

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: Other · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.118

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 studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.240
Teacher spread0.188 · 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
GenreOther

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

Citations4
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicGeochemistry and Geologic MappingFrench-language works237,207