| { |
| "id": "ml19-root", |
| "requirements": "A credible experiment comparing semi-supervised methods (LabelPropagation, LabelSpreading, SelfTrainingClassifier, or equivalents) against a supervised-only baseline under limited-label regimes: methods are implemented correctly, runs cover multiple datasets with repeated seeds, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated alternative semi-supervised methods that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml19-code", |
| "requirements": "Semi-supervised and baseline conditions are implemented correctly with explicit label masking.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml19-code-methods", |
| "requirements": "The submission implements multiple distinct conditions \u2014 typically including at least one graph-based method (LabelPropagation or LabelSpreading), a self-training method, and a supervised-only baseline \u2014 as separate code paths.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml19-code-masking", |
| "requirements": "Label masking for semi-supervised runs is explicit and correct: only a defined labeled fraction retains class labels and the remaining training labels are set to the unlabeled marker.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| }, |
| { |
| "id": "ml19-code-datasets", |
| "requirements": "The submission uses multiple datasets (sklearn built-ins or comparable), with a held-out test split and consistent preprocessing across conditions.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml19-exec", |
| "requirements": "Execution covers multiple label fractions and produces benchmark metrics.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml19-exec-metrics", |
| "requirements": "Execution outputs numeric test accuracy and at least one additional metric (e.g., balanced accuracy or macro-F1) for each implemented (condition, dataset, label-fraction) cell in a machine-readable artifact.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml19-exec-seeds", |
| "requirements": "Each reported cell is aggregated over multiple random seeds with a dispersion measure. Honest small-seed runs with variance reported are 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": "ml19-results", |
| "requirements": "Results address H1/H2/H3 directionally with quantitative comparisons.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml19-result-h1", |
| "requirements": "The submission compares each semi-supervised method versus the supervised-only baseline at a low labeled fraction and conveys whether any method provides a meaningful gain on most datasets \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml19-result-h2", |
| "requirements": "The submission reports a direct LabelSpreading vs LabelPropagation comparison per dataset and conveys whether LabelSpreading is at least competitive on most datasets (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml19-result-h3", |
| "requirements": "The submission reports self-training accuracy at both low and higher labeled fractions and conveys whether the gain with more labels is meaningful (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml19-result-writeup", |
| "requirements": "The README or writeup describes setup and methods, reports per-dataset metrics, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and discusses limitations (dataset scope, seed count variance, sensitivity to masking/hyperparameters). 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 |
| } |
|
|
