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Record W4404195587 · doi:10.1016/j.dendro.2024.126274

CTRing: An R package to extract wood density profiles from computed tomography images of discs and logs

2024· article· en· W4404195587 on OpenAlexafffund
Dipak Mahatara, Filipe Campelo, Laurent Houle, Alain Caron, Julie Barrette, Pierre Francus, Robert J. Schneider

Bibliographic record

VenueDendrochronologia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsGeneral Dynamics (Canada)Ministry of Natural Resources and WildlifeInstitut National de la Recherche ScientifiqueUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of CanadaInstitut national de la recherche scientifique
KeywordsComputed tomographyTomographyGeologyMedicineRadiology

Abstract

fetched live from OpenAlex

Accurately determining the position of pith and accessing tree-ring density profiles, including intra-ring variations, is important for both the forest industry and dendroclimatology. Although several available methods exist for acquiring this information, such as X-ray computed tomography (CT), micro-CT, and X-ray films, the availability of open-source programs for extracting data remains limited. The CTRing package in the R environment integrates a series of functions to detect precisely the pith and tree-ring boundaries and generate tree-ring density profiles using CT images of tree cross sections. Before processing, grey values are transformed into density using a calibration function. Pith position is then detected by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree-ring profiles along the pith-to-bark path of interest are inspected visually, and tree-ring boundaries can be easily added or removed manually via a graphical user interface. After correcting for tree-ring boundaries, the inflection points of a 3rd-degree polynomial obtained from density profiles are used to delimit the earlywood–latewood transition. We tested this package using 60 CT-scanned images of white spruce ( Picea glauca (Moench) Voss) discs collected at various tree heights (0%, 25%, 50% and 75% of the total tree height as well as at 1.3 m). The pith detection function had an average mean error of 0.72 mm with 95% of the automatically detected pith locations that differed by less than 2 mm from their manually located positions. Error decreased toward the apex of the tree. The functions of the CTRing package are flexible and can be easily implemented or adapted. The package could also be used with simple images of discs to obtain ring-width time series; however, this use must be evaluated further. Future work with this package involves assessing the use of low-quality images and ring-porous species.

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)0.004
metaresearch head score (Gemma)0.020
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: Software · Consensus signal: Software
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0500.046

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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
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

Citations3
Published2024
Admission routes2
Has abstractyes

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