growthTrendR R package: A comprehensive toolkit for data processing, quality assessment and statistical analysis of tree-ring data
Bibliographic record
Abstract
We present growthTrendR, a new R package designed to streamline the processing, quality assessment, and statistical analysis of tree-ring data. The package offers tools for data formatting, identification of measurement anomalies, and classification of data quality using spatial comparisons and cross-correlation techniques. It integrates flexible detrending and climate–growth modeling through Generalized Additive Mixed Models (GAMMs), enabling robust analyses of non-linear trends and autocorrelated data. growthTrendR also supports standardized visual reporting, including summaries of data completeness, quality diagnostics, and model performance. Compatible with the widely used .rwl file format and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository, growthTrendR provides a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".