Towards an Engineering Model of Material Characteristics for Input to Ham Transport Simulations - Part 1: An Approach
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
Heat, Air and Moisture (HAM) modelling of building performance is a quite young research subject but the experimental determination of material properties is often based on classical methods. One should review the manner in which we define characteristic material parameters and there is a need to develop an approximation used to generate the required material functions for input to HAM-transport simulations. The paper presents such an approach, called an engineering model for hygrothermal material characterisation. The paper poses the question, how to arrive at input data that can be used for a model based on thermodynamically defined potentials (Only such a model allows introduction of new potential components (freezing depression, osmotic pressure, air pressure, overburden envelope pressure)) (e.g., Grunewald, J. (1997) and Grunewald, J. (1999)) and yet the respective functions used to describe changes in the material response as a function of the variables of state. Such functions should have a reasonable precision and goodness of fit while the number of measured points must be reduced to a minimum. Those measurements should be relatively easy to perform (i.e., they would not require determination of temporal and spatial profiles of moisture). This discussion paper highlights steps already taken (Part 1), and lists issues that need to be resolved before reaching this goal (Part 2).
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 | 0.001 | 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 | 0.001 |
| 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