Towards sustainable North American wood product value chains, part 2: computer vision identification of ring-porous hardwoods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wood identification is vitally important for ensuring the legality of North American hardwood value chains. Computer vision wood identification (CVWID) systems can identify wood without necessitating costly and time-consuming off-site visual inspections by highly trained wood anatomists. Previous work by Ravindran and colleagues presented macroscopic CVWID models for identification of North American diffuse porous hardwoods from 22 wood anatomically informed classes using the open-source XyloTron platform. This manuscript expands on that work by training and evaluating complementary 17-class XyloTron CVWID models for the identification of North American ring porous hardwoods — woods that display spatial heterogeneity in earlywood and latewood pore size and distribution and other radial growth-rate-related features. Deep-learning models trained using 4045 images from 452 ring-porous wood specimens from four xylaria demonstrated 98% five-fold cross-validation accuracy. A field model trained on all the training data and subsequently tested on 198 specimens drawn from two additional xylaria achieved top-1 and top-2 predictions of 91.4% and 100%, respectively, and images devoid of earlywood, latewood, or broad rays did not greatly reduce the prediction accuracy. This study advocates for continued cooperation between wood anatomy and machine-learning experts for implementing and evaluating field-operational CVWID systems.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it