ILLUSTRATIVE · NASA / SVS · NOT ADITYA-L1 DATA
Model cardsmajority
Model card · reference point
Majority
Always predicts the majority class.
What is this?
A model card for Majority, one of eight detectors evaluated on the frozen Aditya-L1 flare benchmark.
Why should I trust it?
Every figure below is read at build time from the committed benchmark artifact; none is transcribed. The judgements are traceable to the written evaluation protocol and the dataset limitations record.
Where can I verify it?
- The validation evidenceComputed from 192,541 held-out predictions
- Dataset limitationsL-1 … L-10
- Evidence traceabilityclaim → artifact → commit
- The raw payloadfindings/models.json
Section 1
Purpose and intended use
Why it is in the benchmark. Shows what accuracy is worth on an imbalanced problem: this model can look excellent on accuracy while detecting nothing at all.
Intended use. Reference point only.
Out of scope. Everything else.
Section 2
Inputs, outputs, training
Inputs. None.
Outputs. A constant.
Training protocol. None.
Operating point. Decision threshold 0.5000, selected on the training period and then frozen.
Section 3
Evaluation
Both tasks, both reported.
M/X NOWCAST · 192,541 held-out minutes · base rate 1.24%
| Metric | Value | What it measures |
|---|---|---|
| ROC-AUC | 0.500 | Ranking quality across all thresholds |
| PR-AUC | 0.012 | The informative curve at a low base rate |
| Precision | 0.000 | Of the minutes flagged, how many were flares |
| Recall | 0.000 | Of the flare minutes, how many were flagged |
| F1 | 0.000 | Harmonic mean of the two above |
| MCC | 0.000 | Correlation, robust to imbalance |
| Brier | 0.012 | Probability accuracy; undefined for hard classifiers |
| Balanced accuracy | 0.500 | Mean of the two class recalls |
- Events detected
- 0 / 82
- Event recall 0.000 · 95% CI 0.000–0.000
- False alarm runs
- 0
- Of 0 alarm runs raised. A run, not a minute — the unit an operator actually responds to.
- ROC-AUC interval
- 0.500–0.500
- 95%, day-block bootstrap.
M/X 30-MIN PREDICTION · 192,541 held-out minutes · base rate 1.29%
| Metric | Value | What it measures |
|---|---|---|
| ROC-AUC | 0.500 | Ranking quality across all thresholds |
| PR-AUC | 0.013 | The informative curve at a low base rate |
| Precision | 0.000 | Of the minutes flagged, how many were flares |
| Recall | 0.000 | Of the flare minutes, how many were flagged |
| F1 | 0.000 | Harmonic mean of the two above |
| MCC | 0.000 | Correlation, robust to imbalance |
| Brier | 0.013 | Probability accuracy; undefined for hard classifiers |
| Balanced accuracy | 0.500 | Mean of the two class recalls |
- Events detected
- 0 / 82
- Event recall 0.000 · 95% CI 0.000–0.000
- False alarm runs
- 0
- Of 0 alarm runs raised. A run, not a minute — the unit an operator actually responds to.
- ROC-AUC interval
- 0.500–0.500
- 95%, day-block bootstrap.
Confusion matrix · M/X NOWCAST
True negative
190,148
False positive
0
False negative
2,393
True positive
0
Section 4
Strengths and failure modes
Strengths
- Makes the case for reporting PR-AUC and event recall instead of accuracy.
Failure modes
- Detects no events, by construction.
Section 5
Limitations and ethical considerations
Ethical considerations. None — it is a ruler, not a detector.
Governing limitation clauses
Clause text is not restated here. It is published in full on the dataset card, from the frozen DATASET_LIMITATIONS_FOR_ML.md report, so there is exactly one copy of every caveat.