Mechanical stress-strain data of Canadian small clear spruce-pine-fir wood
Bibliographic record
Abstract
The dataset present on this repository represents stress-strain data from mechanical testing of clear wood specimens based on ASTMD143-22 standard. This repository contains four type of information: raw data, metadata, Matlab routines, and processed data. Each raw text file contains data in columns. The first column represents the time. The second column represents the load, recorded from the MTS machine load cell. The third column represents the stroke of the actuator. The fourth and last column represents the deformation, measured from either the extensometer or the linear voltage displacement transducer. Next, the metadata file on tests contain information such as the cross-sectional dimensions of each specimen, as well as the calculated ultimate strength and modulus of elasticity. Matlab routines were written to process files. There is one Matlab file for each test type (compression, tension, or shear) and each wood grade. The “.m” Matlab files can be opened with any text editor. Lastly, processed files follow the same blueprint as the raw files. Each processed text file contains data in columns. The first column represents the strain imposed on the specimen. This quantity is dimensionless and is given in percentage. The second column represents the engineering stress resulting from the strain applied on the specimen. The unit for stresses is mega-pascal (MPa).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.036 |
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".