LOG HOUSES IN LES LAURENTIDES.FROM ORAL TRADITION TO AN INTEGRATED DIGITAL DOCUMENTATION BASED ONTHE RE-DISCOVERY OF THE TRADITIONAL CONSTRUCTIVE-GEOGRAPHICAL ‘REPERTOIRES’THROUGH DIGITAL BIM DATA ARCHIVE
Bibliographic record
Abstract
Abstract. This paper presents an integrated approach between digital documentation workflows and historical research in order to document log houses, outstanding example of vernacular architecture in Quebec, focusing on their geometrical-dimensional as well as on the intangible elements associated with these historical structures. The 18 log houses selected in the Laurentians represent the material culture of how settlers adapted to the harsh Quebec environment at the end of the nineteenth century. The essay describes some results coming by professor Mariana Esponda in 2015 (Carleton University) and the digital documentation was carried out through the grant New Paradigm/New Tools for Architectural Heritage in Canada, supported by SSHRC Training Program) (May-August 2016). The workflow of the research started with the digital documentation, accomplished with laser scanning techniques, followed by onsite observations, and archival researches. This led to the creation of an 'abacus', a first step into the development of a territorialhistorical database of the log houses, potentially updatable by other researchers. Another important part of the documentation of these buildings has been the development of Historic Building Information Models fundamental to analyze the geometry of the logs and to understand how these constructions were built. The realization of HBIMs was a first step into the modeling of irregular shapes such as those of the logs – different Level of Detail were adopted in order to show how the models can be used for different purposes. In the future, they can potentially be used for the creation of a virtual tour app for the story telling of these buildings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".