LOG_GROWTH_MODEL
================

Python object: ``phenotypic.schema._log_growth_model.LOG_GROWTH_MODEL``

Fitted parameters and bounds of the logistic growth model.

Output columns are **metric-qualified**: each header is
``LogGrowthModel_<metric>_<parameter>``, where ``<metric>`` records the
measurement the model was fit on (``self.on`` with its category prefix
stripped, e.g. ``Shape_Area`` → ``Area``). For example, fitting on
``Shape_Area`` emits ``LogGrowthModel_Area_r`` (intrinsic growth rate) and
``LogGrowthModel_Area_µmax`` (maximum specific growth rate). The labels
below are the ``<parameter>`` segment; the ``<metric>`` infix is filled in
at fit time.

.. list-table:: Category: **LogGrowthModel**
   :header-rows: 1

   * - Column label
     - Description
     - Type
   * - ``LogGrowthModel_r``
     - The intrinsic growth rate
     - :bdg-ref-success:`Tier 1 · Direct phenotype <measurement-tiers>`
   * - ``LogGrowthModel_K``
     - The carrying capacity
     - :bdg-ref-success:`Tier 1 · Direct phenotype <measurement-tiers>`
   * - ``LogGrowthModel_N0``
     - The initial number of the colony size metric being fitted
     - :bdg-ref-success:`Tier 1 · Direct phenotype <measurement-tiers>`
   * - ``LogGrowthModel_lambda``
     - The regularization factor applied to the max specific growth rate and initial population size
     - :bdg-ref-secondary:`Quality <measurement-classification>`
   * - ``LogGrowthModel_beta``
     - The penalty factor applied to relative difference of the carrying capacity from the largest measurement
     - :bdg-ref-secondary:`Quality <measurement-classification>`
   * - ``LogGrowthModel_µmax``
     - The growth rate of the colony calculated as (K*r)/4
     - :bdg-ref-success:`Tier 1 · Direct phenotype <measurement-tiers>`
   * - ``LogGrowthModel_Kmax``
     - The upper bound of the carrying capacity for model fitting
     - :bdg-ref-secondary:`Quality <measurement-classification>`