Establishing Correspondence in Wood: The Challenge and Some Solutions?
Bibliographic record
Abstract
Establishing correspondence between the upper portion of a white birch sapling, a suspected weapon, and a potential source from a stand of trees was posed to one of us (GMC). A bending force shattered the sapling, precluding physical matching. Three white birch saplings were taken from the same stand of trees in a similar manner. Correspondence was achieved by measuring the width of the annual rings along four radii from a disk cut above and below the break. The regression coefficient of the data from the two disks from the same sapling was r(2) = 0.95. Regressing the upper disk against the lower disk of two other saplings resulted in r(2) values of 0.26 and 0.17, respectively. The various characteristics that are confined to a wood stem as part of its normal process of growth can be used to eliminate candidate saplings and establish correspondence between two pieces of wood.
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 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 | 0.069 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".