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Record W3159244764 · doi:10.21105/joss.02878

Isoreader: An R package to read stable isotope data files for reproducible research

2021· article· en· W3159244764 on OpenAlexfundno aff
Sebastian Kopf, Brett Davidheiser‐Kroll, Ilja Kocken

Bibliographic record

VenueThe Journal of Open Source Software · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersUniversity of Colorado BoulderUniversity of OttawaU.S. Geological SurveyUniversity of WyomingUniversity of WashingtonNational Science Foundation
KeywordsComputer scienceIsotopeR packageDatabaseProgramming languageNuclear physicsPhysics

Abstract

fetched live from OpenAlex

The measurement and interpretation of the stable isotope composition of any material or molecule has widespread application in disciplines ranging from the earth sciences to ecology, anthropology, and forensics.The naturally occurring differences in the abundance of the stable isotopes of carbon, nitrogen, oxygen, and many other elements provide valuable insight into environmental conditions and sources, fluxes, and mechanisms of material transfer.Because isotopic variations in nature are very small, the measurement itself requires cutting edge analytical instrumentation using isotope ratio mass spectrometry (IRMS) as well as rigorous data reduction procedures for calibration and quality control.The isoreader package implements an easily extendable interface for IRMS data from common instrument vendor file formats and thus enables the reading and processing of stable isotope data directly from the source.This provides a foundational tool for platform-independent, efficient and reproducible data reduction.

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)0.012
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.054
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0050.005
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0810.071

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 head0.154
GPT teacher head0.405
Teacher spread0.251 · 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.

Study designNot applicable
DomainReproducibility
GenreSoftware

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

Citations27
Published2021
Admission routes1
Has abstractyes

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