DESA1002 'Nine Quarter City' - <Yuying Liu>
Bibliographic record
Abstract
First of all, I want to appreciate that the design course really gave me a good experience and opportunity of design. I really enjoyed myself in each exercise. Each of them gave me a different experience of designing. I have really get process in model making and my creative improved quite a lot. In addition, I get process in architectural drawing, which has relative to my hard working in each drawing exercise. However, I still need to improve my creative and architectural drawing skillS which are important to an architect. During this semester, one of my favorite exercises is “Dressing the model”. We did cladding on the structure we made before. I think it is a good opportunity for us to make up almost any kinds of cladding that we could imagine by using different kinds of cladding materials. Another reason why I think this is a worthwhile exercise because during this exercise, we carefully thought about what is the material of each part of the model if it is a real building. As our tutor’s requirement I did revise my architectural drawing in each week, as well as the model. It is quite a long time of work, but I think it is really a good opportunity for me to improve myself. At last, I want to say that I really had a good time spending in the studio in this semester. We all got good relationship with each members and our tutor. We also learned from each other by doing the collaborative work.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; both teacher heads agree on what is shown here.
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".