Bibliographic record
Abstract
What This Book IsGeodesies is a technique for making shell-like structures that hold themselves up without supporting columns, by exploiting a threeway grid of tensile forces.They are very strong and can be very large: the geodesic bubble erected to house the United States exhibits at Expo '67 in Montreal encloses some 6 million cubic feet; it is approximately three-fourths of a sphere 250 feet in diameter.They are also very light for what they do: the Montreal bubble weighs about 600 tons, Plexiglas skin and all.It has been calculated that a geodesic sphere approximately one-half mile in diameter would float away like a soap bubble if the air inside it were one degree warmer than the air outside.They have been used as homes, as offices, as fair pavilions, as locomotive roundhouses, as gymnasiums, as auditoriums, as banks, as playground structures for children to climb on, as housings for radar installations on the DEW line, and for observatories buried under snow at the South Pole.Yet, considering their apparent potential, in the quarter-century since Buckminster Fuller introduced them they haven't been used very widely.That is partly because they are mathematically derived structures, and the mathematics hasn't been easily available.Parts for the self-supporting frame must be fabricated to close specifications.The fabrication, with today's technology, is no problem; the problem is learning what the specifications should be.If we know them, we can achieve extraordinary savings of material, weight, and effort.If we don't, we have no resource save one of the conventional methods of building, which by geodesic standards means gross overbuilding.My assumption is that if architects, designers, engineers knew how to get past the first step, which is calculating the pertinent details of a geodesic structure's geometry, they would explore geodesic potentials more than they have.This book shows how to
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.378 | 0.335 |
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".