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Record W6943975729 · doi:10.17632/3fpdwcgtcj

Harvey Lake Itrax XRF-CS

2018· dataset· en· W6943975729 on OpenAlexaboutno aff

Bibliographic record

VenueMendeley Data · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentSediment coreSquare (algebra)High resolutionSample (material)Hydrology (agriculture)

Abstract

fetched live from OpenAlex

Complete dataset for Harvey Lake sediment samples analyzed using Itrax X-ray fluorescence core scanning (XRF-CS). Sediment samples were recovered Harvey Lake, NB, Canada in summer 2016 using an Ekman grab sampler. Twenty cubic cm of sediment was subsampled, centrifuged and dried, loaded into custom-design sample reservoirs, and analyzed using an Itrax XRF-CS device at 0.2 mm resolution for 15 seconds per interval at 30 kV and 19 mA. See Gregory et al., (submitted) for more thorough description of methodology. Elemental concentrations are presented in counts per second MSE = Mean square Error Surface Sample and position are present in mm Mo Inc = Incoherent X-ray Scatter Mo Coh = Coherent X-ray Scatter

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0120.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.152

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 head0.139
GPT teacher head0.357
Teacher spread0.218 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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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