fc405445c6
- Removed obsolete slots: `has_or_had_custodian_observation`, `provider`, and `specificity_annotation`. - Updated `has_or_had_score` slot to use `SpecificityScore` class and modified its description and examples. - Added new slots: `end_seconds`, `end_time`, `has_archive_path`, `has_or_had_custodian_name`, `protocol_name`, and `protocol_version`. - Introduced a script `check_annotation_types.py` to validate the presence and structure of `custodian_types` in YAML files. - Added a script `update_specificity.py` to automate updates related to `SpecificityAnnotation` to `SpecificityScore`.
80 lines
3.4 KiB
YAML
80 lines
3.4 KiB
YAML
id: https://nde.nl/ontology/hc/class/DetectionThreshold
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name: DetectionThreshold
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title: Detection Threshold Class
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prefixes:
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linkml: https://w3id.org/linkml/
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hc: https://nde.nl/ontology/hc/
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dqv: http://www.w3.org/ns/dqv#
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schema: http://schema.org/
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prov: http://www.w3.org/ns/prov#
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dcterms: http://purl.org/dc/terms/
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crm: http://www.cidoc-crm.org/cidoc-crm/
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skos: http://www.w3.org/2004/02/skos/core#
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rdfs: http://www.w3.org/2000/01/rdf-schema#
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org: http://www.w3.org/ns/org#
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xsd: http://www.w3.org/2001/XMLSchema#
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default_prefix: hc
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imports:
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- linkml:types
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- ../metadata
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- ../slots/has_or_had_description
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- ../slots/has_or_had_label
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- ../slots/has_or_had_type
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classes:
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DetectionThreshold:
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class_uri: dqv:QualityMeasurement
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description: "Configuration for detection thresholds in analysis pipelines.\n\n\
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**DEFINITION**:\n\nDetectionThreshold represents the confidence threshold settings\
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\ used to filter\ndetection results. CV models output confidence scores; thresholds\
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\ determine\nwhich detections are included in results.\n\n**Threshold Levels**:\n\
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\n| Threshold | Range | Use Case |\n|-----------|-------|----------|\n| HIGH_PRECISION\
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\ | 0.9+ | Production display, high confidence |\n| BALANCED | 0.7-0.9 | General\
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\ use, balance precision/recall |\n| HIGH_RECALL | 0.5-0.7 | Research, review,\
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\ catch more |\n| RAW | < 0.5 | Unfiltered, needs post-processing |\n\n**Ontological\
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\ Alignment**:\n- **DQV**: `dqv:QualityMeasurement` - quality metric for data\
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\ assessment\n- **PROV-O**: Threshold as parameter of detection activity\n\n\
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**Migrated From** (per slot_fixes.yaml):\n- `detection_threshold` (float) now\
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\ uses:\n - `filters_or_filtered` → DetectedEntity\n - `has_or_had_treshold`\
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\ → DetectionThreshold (this class)\n\n**Usage Pattern**:\n```\nVideoAnnotation\n\
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\ └── filters_or_filtered → DetectedEntity\n └── has_or_had_treshold\
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\ → DetectionThreshold\n ├── threshold_value: 0.5\n \
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\ └── threshold_type: MINIMUM\n```\n"
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exact_mappings:
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- dqv:QualityMeasurement
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close_mappings:
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- schema:QuantitativeValue
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related_mappings:
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- prov:SoftwareAgent
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slots:
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- has_or_had_label
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- has_or_had_description
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- has_or_had_type
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slot_usage:
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has_or_had_label:
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examples:
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- value: High Precision Threshold
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- value: Research Mode Threshold
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has_or_had_type:
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examples:
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- value: HIGH_PRECISION
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- value: BALANCED
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annotations:
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custodian_types: '["D"]'
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custodian_types_rationale: Detection thresholds apply to digital platforms with
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automated analysis
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specificity_score: 0.75
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specificity_rationale: Fairly specific to video/media analysis contexts
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comments:
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- Represents detection threshold configuration
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- Migrated from detection_threshold slot per slot_fixes.yaml
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- threshold_value is the numeric confidence cutoff
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- threshold_type indicates how threshold is applied
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examples:
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- value:
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has_or_had_label: Standard Detection
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- value:
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has_or_had_label: High Precision
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has_or_had_description: For production display requiring high confidence
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- value:
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has_or_had_label: Research Mode
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has_or_had_description: Low threshold to maximize recall for research
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