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
Computer modelling is an essential part in the analysis and design of residential and commercial buildings as well as long-span structures. It is also a valuable tool in the development and optimisation of wood-based products, connections, and systems. A survey shows that practicing engineers are typically unfamiliar with timber structure modelling, and researchers generally lack resources for advanced modelling of timber systems. A global collaboration, including research institutes, consulting firms, manufactures, software companies, and government and associations, was initiated by FPInnovations in 2020 to develop a guide for supporting the application of numerical modelling on analysis and design of timber structures, and development and optimisation of wood-based products and systems. The guide -Modelling Guide for Timber Structures -covers a wide range of practical and advanced modelling topics, including a comparison (in terms of modelling) among timber, steel, and concrete structures; key modelling principles, methods, and techniques that are specific to timber structures; modelling approaches and considerations for wood-based components, connections, and assemblies; and analysing approaches and considerations for timber structures during progressive collapse, wind, and earthquake events. This paper provides a high-level overview of this guide, with the goal of assisting practicing engineers in application of computer modelling to timber structures, enriching researchers' resources for advanced computer modelling of timber systems; and assisting software companies to identify the gaps and upgrade programs accordingly to accommodate advanced computer modelling of timber structures.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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