| { |
| "id": "ml05-root", |
| "requirements": "A credible experiment studying how linear (PCA) vs non-linear (t-SNE / UMAP-like) dimensionality reduction preserves cluster structure on synthetic high-dimensional datasets: methods share a consistent evaluation pipeline, execution covers multiple datasets with repeated seeds, and conclusions address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. UMAP may be unavailable in the environment \u2014 a documented fallback (e.g., Isomap, LLE) that tests the same scientific question should be credited rather than penalized.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml05-code", |
| "requirements": "The dimensionality-reduction and clustering conditions are implemented in a way that supports a fair comparison.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml05-code-conditions", |
| "requirements": "The submission implements the main reduction conditions relevant to the hypotheses \u2014 a linear method (PCA), at least one nonlinear method (t-SNE, UMAP, or a documented substitute such as Isomap), and preferably a no-reduction identity baseline \u2014 as distinct code paths.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml05-code-data", |
| "requirements": "The submission generates multiple synthetic high-dimensional datasets that stress different geometries (e.g., isotropic blobs, anisotropic blobs, embedded non-linear manifolds such as moons) with reproducible seeds and retained ground-truth labels.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml05-code-pipeline", |
| "requirements": "A consistent post-reduction clustering backend (e.g., KMeans with k matching the true cluster count) and a shared metric-computation pipeline are applied uniformly across conditions.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml05-exec", |
| "requirements": "Execution outputs quantitative cluster-quality and runtime metrics.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml05-exec-metrics", |
| "requirements": "Execution produces a machine-readable metrics artifact with numeric cluster-quality scores (silhouette, ARI, or equivalents) and a wall-clock reduction-time measure per (dataset, condition) cell.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml05-exec-seeds", |
| "requirements": "Reported metrics are aggregated over multiple random seeds per (dataset, condition) with some dispersion measure. More seeds are better, but an honest small-seed run with variance reported is preferable to a single run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml05-results", |
| "requirements": "Results are analyzed against the hypotheses with interpretable evidence.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml05-result-h1", |
| "requirements": "The submission compares nonlinear methods vs PCA on the nonlinear-manifold dataset using a cluster-quality metric (silhouette or ARI) and conveys whether the nonlinear methods produce meaningfully better cluster separation \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml05-result-h2", |
| "requirements": "The submission discusses PCA vs the best nonlinear method on the linearly-separable blobs dataset and conveys whether PCA is competitive (comparable quality) in that regime.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml05-result-h3", |
| "requirements": "The submission reports runtime rankings across methods and conveys whether PCA is markedly faster than the nonlinear methods on most datasets.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml05-result-writeup", |
| "requirements": "The README or writeup describes setup, reports the key metric numbers, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations such as synthetic-only scope, hyperparameter sensitivity, or substitutions for unavailable methods. No strict word-count requirement.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
