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Slot: Cross-Validation R² (exposure_model_cross_validation_r2)

Model cross-validation R², where reported by the producer.

Tier: recommended

Why this slot matters

The single most useful one-number quality signal for a modeled product. Omitting it leaves downstream users with no quantitative basis for weighing one exposure product against another or for propagating model skill into their error budgets.

In plain terms

Cross-validation R² is a 0-to-1 score of how well the model predicted values it had never seen during training — closer to 1 is better. A score of 0.90 means the model captures most of the real variation; a low score means treat the numbers with caution.

URI: envar:slot/exposure_model_cross_validation_r2

Applicable Classes

Name Description Modifies Slot
ExposureModel The model class that produced the values (interpolation, reanalysis, ML, stat... no

Properties

Type and Range

Property Value
Range Float
Domain Of ExposureModel

Cardinality and Requirements

Property Value

Examples

Value
0.90

See Also

Identifier and Mapping Information

Annotations

property value
tier recommended
justification The single most useful one-number quality signal for a modeled product. Omitting it leaves downstream users with no quantitative basis for weighing one exposure product against another or for propagating model skill into their error budgets.
explanation Cross-validation R² is a 0-to-1 score of how well the model predicted values it had never seen during training — closer to 1 is better. A score of 0.90 means the model captures most of the real variation; a low score means treat the numbers with caution.
covered_by None

Schema Source

Mappings

Mapping Type Mapped Value
self envar:exposure_model_cross_validation_r2
native envar:exposure_model_cross_validation_r2

LinkML Source

name: exposure_model_cross_validation_r2
annotations:
  tier:
    tag: tier
    value: recommended
  justification:
    tag: justification
    value: The single most useful one-number quality signal for a modeled product.
      Omitting it leaves downstream users with no quantitative basis for weighing
      one exposure product against another or for propagating model skill into their
      error budgets.
  explanation:
    tag: explanation
    value: Cross-validation R² is a 0-to-1 score of how well the model predicted values
      it had never seen during training — closer to 1 is better. A score of 0.90 means
      the model captures most of the real variation; a low score means treat the numbers
      with caution.
  covered_by:
    tag: covered_by
    annotations:
      omop_gaia:
        tag: omop_gaia
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: GAIA carries no model cross-validation R2; no such slot exists
              in gaiaCatalog or gaia-db.
      degauss:
        tag: degauss
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: DeGAUSS carries no model cross-validation R2.
      amadeus:
        tag: amadeus
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: Amadeus carries no model cross-validation R2.
      cher:
        tag: cher
        annotations:
          extent:
            tag: extent
            value: absent
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: C-HER carries no source-model skill/cross-validation field.
      codata:
        tag: codata
        annotations:
          extent:
            tag: extent
            value: out_of_layer
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: no column
          note:
            tag: note
            value: Model cross-validation skill is instance-layer quality metadata
              the CDIF / DDI-CDI conceptual and represented layers do not carry.
description: Model cross-validation R², where reported by the producer.
title: Cross-Validation R²
examples:
- value: '0.90'
  description: reported for ACAG V5.GL satellite-derived PM2.5
from_schema: https://w3id.org/linkml/microschemas/envar
see_also:
- https://en.wikipedia.org/wiki/Coefficient_of_determination
rank: 1000
domain_of:
- ExposureModel
range: float