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Record W6924664713 · doi:10.1594/ieda/111466

Radiogenic isotope ratios (Sr, Nd, Pb, Hf) of volcanic rocks from the Galapagos Archipelago / Galapagos Islands

2019· dataset· en· W6924664713 on OpenAlexaboutno aff

Bibliographic record

VenueEarthChem Library · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRadiogenic nuclideArchipelagoIsotopeVolcanoVolcanic rockIsotope geochemistryTrace elementStable isotope ratio

Abstract

fetched live from OpenAlex

This dataset contains Sr, Nd, Pb, and Hf isotopic ratios of volcanic rocks from the Galapagos Archipelago, selected from across the region to be representative of the geochemical variation in the hotspot system. The goal of the project is twofold: a) to assemble a dataset of radiogenic isotope ratios performed at a high and consistent level of precision; and b) to compare radiogenic isotope ratios with recent Hawaii isotopic signatures (e.g., Weis et al., 2011). To build a complete dataset, we selected the samples from across the archipelago from existing collections, in addition to new samples from Espanola, San Cristobal, and Santa Cruz Islands. Any samples that were missing published major and full suites of trace element data were also analyzed for those missing data (submitted separately). All of the radiogenic isotope ratios were performed at the Pacific Centre for Isotopic and Geochemical Research at the University of British Columbia in Vancouver, Canada following procedures described in Weis et al. (2006, 2007) and Nobre Silva et al. (2013).

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.017

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 head0.016
GPT teacher head0.222
Teacher spread0.207 · 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
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
Published2019
Admission routes1
Has abstractyes

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