scenario_id string | scenario_text string | claim string | label int64 |
|---|---|---|---|
train_001 | Demand is rising, inventory is falling, supplier lead times are increasing, and storage capacity is fixed. | The system is under significant constraint pressure. | 1 |
train_002 | Demand is stable, inventory is healthy, suppliers are reliable, and storage capacity is sufficient. | The system is under significant constraint pressure. | 0 |
train_003 | A clinic has rising patient volume, staff shortages, and limited treatment rooms. | The clinic is under significant constraint pressure. | 1 |
train_004 | A clinic has normal patient volume and available staffing capacity. | The clinic is under significant constraint pressure. | 0 |
train_005 | A project faces a fixed deadline, increasing scope, and no additional staffing. | The project is under significant constraint pressure. | 1 |
train_006 | A project has schedule buffer, stable scope, and flexible staffing. | The project is under significant constraint pressure. | 0 |
train_007 | A database is approaching storage limits while query volume continues to grow. | The database is under significant constraint pressure. | 1 |
train_008 | A database has ample storage headroom and stable query volume. | The database is under significant constraint pressure. | 0 |
train_009 | A patient has worsening symptoms while treatment options become limited. | The patient is under significant constraint pressure. | 1 |
train_010 | A patient has improving symptoms and multiple treatment options remain available. | The patient is under significant constraint pressure. | 0 |
train_011 | A warehouse faces rising orders, equipment downtime, and staffing shortages. | The warehouse is under significant constraint pressure. | 1 |
train_012 | A warehouse has stable demand and reserve staffing available. | The warehouse is under significant constraint pressure. | 0 |
train_013 | A support team faces growing backlog while staffing levels decline. | The support system is under significant constraint pressure. | 1 |
train_014 | A support team has low backlog and available reserve capacity. | The support system is under significant constraint pressure. | 0 |
train_015 | A bridge experiences rising traffic loads while maintenance funding is delayed. | The bridge is under significant constraint pressure. | 1 |
train_016 | A bridge operates below design load and maintenance is current. | The bridge is under significant constraint pressure. | 0 |
train_017 | Cash reserves are declining while obligations increase and revenue falls. | The organization is under significant constraint pressure. | 1 |
train_018 | Cash reserves are growing while obligations remain stable. | The organization is under significant constraint pressure. | 0 |
train_019 | A model faces increasing request volume while inference resources remain fixed. | The model deployment is under significant constraint pressure. | 1 |
train_020 | A model deployment has stable demand and scalable infrastructure. | The model deployment is under significant constraint pressure. | 0 |
What this dataset does
This dataset tests whether a model can detect constraint pressure.
The task is simple:
Given a scenario and a constraint-pressure claim, predict whether the claim is supported.
Core stability idea
Constraint pressure emerges when demands increase while available degrees of freedom decrease.
Pressure alone is not enough.
Constraint pressure arises when the system has fewer viable options, less buffer, less flexibility, or reduced recovery margin.
High constraint pressure often precedes:
- instability
- collapse
- bottlenecks
- cascading failure
- regime transition
Prediction target
Binary label:
- 1 = significant constraint pressure is present
- 0 = significant constraint pressure is not present
Row structure
Each row contains:
- scenario_id
- scenario_text
- claim
- label
Files
- data/train.csv
- data/test.csv
- scorer.py
- README.md
Evaluation
Create a predictions CSV with:
scenario_id,prediction
test_001,1
test_002,0
Run:
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 recognize increasing pressure combined with shrinking optionality.
The hidden value is in detecting narrowing control surfaces, shrinking margins, bottleneck formation, and reduced adaptive freedom.
License
MIT
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