Linear window correlation: new image processing based approach to strain distribution analysis of wood<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.
Bibliographic record
Abstract
An in-depth understanding of the response of wood under compression/densification calls for characterization of strain distribution over the wood. Recently, an image processing technique has been used for strain evaluation on wood, as the traditional devices are not able to measure irregularly distributed strain field. Unfortunately, the classic image correlation algorithm is rather time consuming and could take up to days to compute the final result. In this paper, an improved image correlation algorithm with linearly changed searching region during the correlation seeking is presented. The processing speed is increased by over 1800 times while keeping the high accuracy of the displacement measurement. In addition, full feasibility tests were conducted on balsam fir ( Abies balsamea (L.) Mill.) and eastern white pine ( Pinus strobus L.) samples. The results demonstrate the high robustness, efficiency, and accuracy of the proposed method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".