Model card — default mode classifier
====================================

This card documents the **experimental** learned classifier shipped with VANE. It
follows the spirit of a model card: what the model is, how it was built, where it
may be used, and where it must not be trusted.

.. warning::

   The default model is trained on **synthetic** priors, not measured turbine data.
   Its probabilities are indicative, not validated. Constructing the classifier emits
   an :class:`~vane.ai.ExperimentalWarning`. The rule-based physical label and the
   deterministic blade taxonomy are the primary, validated identification path.

Model details
-------------

* **Type:** Gaussian-process classifier (scikit-learn ``GaussianProcessClassifier``
  with a constant × RBF kernel).
* **Task:** map a mode's feature vector to a physical degree-of-freedom category, with
  a calibrated per-class probability used as a confidence.
* **Inputs:** the scale-independent features of :mod:`vane.ai.features` — natural
  frequency, damping ratio, and the per-category participation fractions.
* **Outputs:** a predicted :class:`~vane.config.dof_map.DofCategory` and a full class
  probability vector.

Training data
-------------

* **Source:** :func:`~vane.ai.synthetic_training_set` generates labelled feature
  vectors from category prototypes with controlled noise. It is a *bootstrap*, not a
  measured corpus.
* **Determinism:** the generator is seeded; the same seed yields the same training
  set and model.

Intended use
------------

* Exploratory, uncertainty-aware cross-checking of the rule-based labels.
* Research and development of the identification pipeline.

Out-of-scope use
----------------

* Engineering or certification decisions based on the learned label alone.
* Any claim of validated accuracy on measured turbines.

Metrics
-------

No held-out accuracy or calibration metrics on measured data are published, because
no measured labelled corpus is available yet. Reported confidences are the model's
own class probabilities on synthetic data and should be read as relative, not
absolute.

Path to graduation
------------------

The classifier graduates from experimental once it is trained and calibrated on a
labelled, multi-turbine measured corpus, with published held-out accuracy and
calibration curves and versioned model artefacts. Until then it remains gated behind
the :class:`~vane.ai.ExperimentalWarning`.