Bibliographic record
Abstract
Digital Wood seeks to develop lighter, more delicate, and more efficient structural framing members by computationally realigning wood grain to the corresponding principle stress lines acting within the member while it's under load. This will be achieved by utilizing computational analysis and design software in conjunction with digital milling tools to produce molds, jigs and formwork required for fabrication. Once fabricated, the resulting framing member will retain the performance properties of the original, but in a fraction of the material and thus a fraction of the weight. This materials research thesis will utilize the intrinsic mechanical properties of wood fibre to produce a superior structural framing system. Digital tools of design and fabrication are capable of not only uncovering these properties, but assisting in precision milling required for grain realignment to be viable option in the construction industry. Through this research, I intend to develop a wood-specific structural design methodology that harnesses the strength properties of wood's anisotropic nature. The structural system developed from this working methodology will carry loads and distribute forces in a manner that closely resembles that which a living tree would also carry loads and distribute forces. Ultimately Digital Wood will explore alternative methods of designing and fabricating structural wood components that utilize the properties of wood grain in the most appropriate manner possible and make a meaningful contribution to both the fields of architecture and structural engineering that affirms the versatility and dependability of wood building products.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".