scenario_id string | scenario_text string | claim string | label int64 |
|---|---|---|---|
train_001 | The service outage can be resolved with a simple restart and no data recovery. | The recovery energy requirement is low. | 1 |
train_002 | The service outage requires rebuilding infrastructure, restoring backups, and manual reconciliation. | The recovery energy requirement is low. | 0 |
train_003 | A patient needs short-term fluids and observation to return to baseline. | The recovery energy requirement is low. | 1 |
train_004 | A patient requires prolonged intensive care and rehabilitation. | The recovery energy requirement is low. | 0 |
train_005 | A warehouse restores operations by clearing a small backlog. | The recovery energy requirement is low. | 1 |
train_006 | A warehouse must rebuild inventory, staffing, and supplier relationships. | The recovery energy requirement is low. | 0 |
train_007 | A project returns to schedule through minor reprioritization. | The recovery energy requirement is low. | 1 |
train_008 | A project requires major scope reduction and contract renegotiation. | The recovery energy requirement is low. | 0 |
train_009 | A machine returns to operation after replacing a worn component. | The recovery energy requirement is low. | 1 |
train_010 | A machine requires major rebuild after catastrophic failure. | The recovery energy requirement is low. | 0 |
train_011 | A support team recovers after one additional shift clears backlog. | The recovery energy requirement is low. | 1 |
train_012 | A support team requires months of hiring and retraining. | The recovery energy requirement is low. | 0 |
train_013 | A database recovers after restoring one corrupted table. | The recovery energy requirement is low. | 1 |
train_014 | A database requires full migration and data reconstruction. | The recovery energy requirement is low. | 0 |
train_015 | A bridge issue is resolved through routine maintenance. | The recovery energy requirement is low. | 1 |
train_016 | A bridge issue requires major structural reconstruction. | The recovery energy requirement is low. | 0 |
train_017 | A logistics disruption is resolved through temporary rerouting. | The recovery energy requirement is low. | 1 |
train_018 | A logistics disruption requires redesigning the entire distribution network. | The recovery energy requirement is low. | 0 |
train_019 | A model issue is fixed through prompt adjustment. | The recovery energy requirement is low. | 1 |
train_020 | A model issue requires retraining and redeployment. | The recovery energy requirement is low. | 0 |
What this dataset does
This dataset tests whether a model can estimate recovery energy.
The task is simple:
Given a scenario and a recovery-energy claim, predict whether the claim is supported.
Core stability idea
Recovery energy measures the effort required to return a system to a healthy operating state.
Recovery energy may include:
- time
- money
- labor
- expertise
- infrastructure
- coordination
Low recovery energy systems can recover quickly and cheaply.
High recovery energy systems require substantial intervention to restore stability.
Prediction target
Binary label:
- 1 = recovery energy requirement is low
- 0 = recovery energy requirement is high
Row structure
Each row contains:
- scenario_id
- scenario_text
- claim
- label
Files
- data/train.csv
- data/test.csv
- scorer.py
- README.md
Evaluation
python scorer.py --predictions predictions.csv --truth data/test.csv
Structural Note
This dataset is intentionally small.
Its purpose is to test whether a model can distinguish easy recovery from costly recovery.
The hidden value is in recognizing restoration effort, recovery complexity, resource burden, and repair magnitude.
License
MIT
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