BIKED: A Dataset for Computational Bicycle Design with Machine Learning\n Benchmarks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we present "BIKED," a dataset comprised of 4500 individually\ndesigned bicycle models sourced from hundreds of designers. We expect BIKED to\nenable a variety of data-driven design applications for bicycles and support\nthe development of data-driven design methods. The dataset is comprised of a\nvariety of design information including assembly images, component images,\nnumerical design parameters, and class labels. In this paper, we first discuss\nthe processing of the dataset, then highlight some prominent research questions\nthat BIKED can help address. Of these questions, we further explore the\nfollowing in detail: 1) Are there prominent gaps in the current bicycle market\nand design space? We explore the design space using unsupervised dimensionality\nreduction methods. 2) How does one identify the class of a bicycle and what\nfactors play a key role in defining it? We address the bicycle classification\ntask by training a multitude of classifiers using different forms of design\ndata and identifying parameters of particular significance through\npermutation-based interpretability analysis. 3) How does one synthesize new\nbicycles using different representation methods? We consider numerous machine\nlearning methods to generate new bicycle models as well as interpolate between\nand extrapolate from existing models using Variational Autoencoders. The\ndataset and code are available at http://decode.mit.edu/projects/biked/.\n
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it