Slot: Exposure Model Type (exposure_model_type)

The class of model that produced the values. Daymet = spatial_interpolation; NARR / ERA5 = reanalysis; GridMET = statistical_blend; Brokamp PM = single_machine_learning; Di et al. PM = ensemble_machine_learning.

Tier: core

Why this slot matters

A measured value, an interpolated value, and an ML-predicted value have different error structures and cannot be pooled naively; this is the field that tells them apart. Without it, users cannot judge whether values are measurements or predictions.

In plain terms

Most "exposure" numbers were never measured at your door: a model estimates them from weather stations, satellites, or physics equations. This field says which kind of machinery produced the number, so you know how much to trust it and what could go wrong.

URI: envar:slot/exposure_model_type

Applicable Classes

Name	Description	Modifies Slot
ExposureModel	The model class that produced the values (interpolation, reanalysis, ML, stat...	yes

Properties

Type and Range

Property	Value
Range	ExposureModelTypeEnum
Domain Of	ExposureModel

Cardinality and Requirements

Property	Value

Examples

Value
spatial_interpolation
satellite_retrieval

Identifier and Mapping Information

Annotations

property	value
tier	core
justification	A measured value, an interpolated value, and an ML-predicted value have different error structures and cannot be pooled naively; this is the field that tells them apart. Without it, users cannot judge whether values are measurements or predictions.
explanation	Most "exposure" numbers were never measured at your door: a model estimates them from weather stations, satellites, or physics equations. This field says which kind of machinery produced the number, so you know how much to trust it and what could go wrong.
covered_by	None

Schema Source

from schema: https://w3id.org/linkml/microschemas/envar

Mappings

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

LinkML Source

name: exposure_model_type
annotations:
  tier:
    tag: tier
    value: core
  justification:
    tag: justification
    value: A measured value, an interpolated value, and an ML-predicted value have
      different error structures and cannot be pooled naively; this is the field that
      tells them apart. Without it, users cannot judge whether values are measurements
      or predictions.
  explanation:
    tag: explanation
    value: 'Most "exposure" numbers were never measured at your door: a model estimates
      them from weather stations, satellites, or physics equations. This field says
      which kind of machinery produced the number, so you know how much to trust it
      and what could go wrong.'
  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 has no first-class exposure-model-type slot; it is present
              only via the EnVar JSON-LD extension. Natively GAIA carries only a free-text
              measurement_technique.
      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 exposure-model-type slot.
      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 exposure-model-type slot.
      cher:
        tag: cher
        annotations:
          extent:
            tag: extent
            value: partial
          status:
            tag: status
            value: asserted
          where:
            tag: where
            value: table_dictionary data_level (mXX = modeled data)
          note:
            tag: note
            value: C-HER's data_level code flags modeled data (mXX) and distinguishes
              calculated (dXX) from modeled, but does not name the model class (reanalysis
              vs interpolation).
      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: The exposure-model class is instance-layer methodology that CDIF
              / DDI-CDI conceptual and represented layers explicitly do not carry
              (related-approaches.md §4).
description: The class of model that produced the values. Daymet = `spatial_interpolation`;
  NARR / ERA5 = `reanalysis`; GridMET = `statistical_blend`; Brokamp PM = `single_machine_learning`;
  Di et al. PM = `ensemble_machine_learning`.
title: Exposure Model Type
examples:
- value: spatial_interpolation
  description: Daymet V4 daily Tmax (station observations interpolated to a 1 km grid)
- value: satellite_retrieval
  description: ACAG V5.GL satellite-derived annual PM2.5
from_schema: https://w3id.org/linkml/microschemas/envar
see_also:
- https://daymet.ornl.gov/
- https://www.climatologylab.org/gridmet.html
- https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
rank: 1000
domain_of:
- ExposureModel
range: ExposureModelTypeEnum