R-Tools for Statistical Evaluation and Exploration of Granulometric and Bulk Geochemical Data
Bibliographic record
Abstract
This publication contains tools for statistical evaluation and exploration of data published by Radosavljevic et al. (2016). These data contain bulk geochemistry data (total organic carbon, nitrogen, stable carbon isotope) and granulometry of nearshore samples in the vicinity of Herschel Island, Yukon, Canadian Beaufort Sea. In addition, the functions of the script herein provide a means for summaries and comparison with terrestrial (Couture, 2010; Tanski et al., 2017; Obu et al., 2016) and marine (a subset of Naidu et al., 2000) data. The tools are contained in a script written for the R software environment for statistical computing and graphics. The script (sediments_geochemistry_plots_and_summaries.r) is richly documented and explains the functionality. Each data file also contains a description of the data in a comma separated file (csv). The functions of the script are: myinteract() - interactive mode mysum() - provides numerical summaries for WBP and TB, a box plot and runs a Two-sided Mann-Whitney-Wilcoxon test myloc() - provides numerical summaries and comparisons among the current study, marine, and terrestrial samples, a box plot and runs a Two-sided Mann-Whitney-Wilcoxon test myseds() - provides numerical summaries and comparisons of grain size data among the current study mycums() - plots cumulative frequency curves of grain size distributions by transect The package contains (included in the zip folder): sediments_geochemistry_plots_and_summaries.r - script file geochemistry_data_including_other_studies.csv - contains data by Radosavljevic et al. (2016) and other studies in the region VolFrequenciesCoordsTransects.csv - contains volumetric grain size frequencies granulometry_stats.csv - contains summary statistics of grain size data TransectSampleIndex.csv - provides an index of transects TransectMap.png - an overview map of sample transects
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.137 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.089 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".