[
  {
    "name": "video_availability_prediction",
    "description": "Predict whether a course includes video lectures from its text description",
    "type": "binary_classification",
    "metric": "F1 (macro), Accuracy",
    "n_total": 1137,
    "n_train": 822,
    "n_dev": 176,
    "n_test": 139,
    "positive_rate": 1.0
  },
  {
    "name": "cross_market_occupation_matching",
    "description": "Match occupations between O*NET and ESCO systems",
    "type": "retrieval / matching",
    "metric": "Recall@k, MRR",
    "n_total": 3039,
    "n_train": 2124,
    "n_dev": 423,
    "n_test": 492
  },
  {
    "name": "ai_exposure_prediction",
    "description": "Predict AI automation exposure score (1-10) from occupation text",
    "type": "regression",
    "metric": "R², MAE, Spearman rho",
    "n_total": 0,
    "n_train": 0,
    "n_dev": 0,
    "n_test": 0,
    "label_mean": 0,
    "label_std": 0
  },
  {
    "name": "cross_lingual_alignment",
    "description": "Align same occupations described in different languages",
    "type": "retrieval / bitext mining",
    "metric": "Recall@1, Recall@5, MRR",
    "n_total": 1500,
    "n_train": 1067,
    "n_dev": 204,
    "n_test": 229,
    "lang_pairs": [
      "en↔uk",
      "sv↔uk",
      "en↔sv"
    ]
  },
  {
    "name": "temporal_drift_prediction",
    "description": "Predict future occupation embedding from 3 historical versions",
    "type": "regression (embedding prediction)",
    "metric": "Cosine similarity, L2 distance to ground truth",
    "n_total": 1016,
    "n_train": 693,
    "n_dev": 176,
    "n_test": 147
  }
]