Bibliographic record
Abstract
Introduction * Carter-Tucker House, Australia (Sean Godsell) * Private House, Denmark (Henning Larsen Tegnestue * Dirty House, London (David Adjaye) * De Blas House, Spain (Alberto Campo Baeza * Weathering Steel House, Toronto (Shim Sutcliffe) * Tyler House, Arizona, USA (Rick Joy) * Weiss House, Cabo San Lucas, Mexico (Steven Harris) * Casa Equis, Peru (Barcley & Crousse) * Alonso-Planas House, Barcelona (Carlos Ferrater) * Kohler Residence, New Brunswick, Canada (Julie Snow) * Mountain Tree Guest House, Georgia, USA (Mack Scogin Merrill Elam) * Zigzag Cabin, Australia (Drew Heath) * Fletcher-Page House, Australia (Glenn Murcutt) * T House, Tokyo (Toyo Ito) * C House and Y House, Tokyo (Kei'ichi Irie) * Small House, Tokyo * Kazuyo Sejima/SANAA * Wall House 2, The Netherlands (John Hejduk) * Private House, Massachusetts, USA (Will Bruder) * Compound, Casey Key, Florida, USA (Toshiko Mori) * Flatz House, Leichtenstein (Baumschlager/Eberle) * Picture Window House, Japan (Shigeru Ban) * Suitcase House, China (Gary Chang) * Bamboo Wall House, China (Kengo Kuma) * Father's House in Jade Mountain, China (MADA s.p.a.m.) * Straw Bale House, London (Sarah Wigglesworth) * Sobek House, Stuttgart (Werner Sobek) * Pedernal House, Mexico City (Adriana Monroy Noriega) * f2 House, Mexico City (Adria Broid Rojkind) * Haus Nenning, Austria (Cukrowicz-Nachbaur) * Neugebauer House, Naples, Florida, USA (Richard Meier) * Casa Ponce, Buenos Aires (Mathias Klotz) * Chicago House, Chicago, Illinois, USA (Tadao Ando) * Haus Zerlauth, Austria (D.I. Hermann Kaufmann) * Endmatter
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.662 | 0.318 |
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".