Проведение тепловизионного обследования как способ выявления дефектов конструкций строящихся объектов
Bibliographic record
Abstract
The article deals with the conduct and analysis of thermal imaging survey is being built teaching and laboratory building Mirny polytechnic institute (branch) of the North-Eastern federal university named after M.K. Ammosov in 10 quarter of the Mirny. Analyzes regulations that promote the growth of interest in the thermal imaging survey. Studied national and international standards governing the procedure for examination of buildings and structures. Modern infrared imager SAT-G90–5 firm SAT Infrared Technology (Japan) has been used to study. Measurements were made in the northern building and climate zone, with an estimated winter outdoor temperature – minus 50°C. As the results of the measurements are presented infrared images of the surface of the outer building under construction teaching and laboratory building, which can be seen in the violation of thermal insulation and insulation defects at the joints of the walls and windows. The conclusion about the need to develop guidelines on thermal imaging survey (energy audit) of building 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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".