Investigating the characterisation of temperatures within New Zealand buildings : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Physics at Massey University
Bibliographic record
Abstract
The variations in indoor temperatures between New Zealand buildings can be due to \ndifferences in the behaviour of the occupants (for example how frequently the building is \noccupied) or due to physical differences between the buildings (such as differing insulation \nlevels or degree of shading). \nThis thesis will look at some physical processes that give rise to temperature variations and \nwill look to see how the overall variation in temperatures is affected by these physical \nproperties. \nOne systematic physical process affecting the indoor temperature within a building occurs \nwhen the area being considered is small (such as the living room of a house) and the degree \nof heat flow into the room is reasonably large, the temperature within the room will then have \na tendency to increase with height resulting in a vertical temperature gradient. Detailed \nvertical temperature distributions are examined for two houses. \nAnother source of variation is the differences in temperatures throughout a building. This \nexamines the extent to which buildings are only partially heated. This has briefly been \nexamined in this thesis by examining the contrasts between the temperature measurements throughout a set of nine houses. \nSome sources of physical temperature variation within a building can be unpredictable. \nLocalised temperature anomalies can be due to the presence of specific heat flows \n(frequently from household appliances). This thesis contains examples of these localised \nsources and provides guidance for placing temperature sensors to minimise localised effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".