'calibration' 검색 결과

Use when a task needs the judgment of a Calibration Technologist/Technician — computing a Test Uncertainty Ratio (TUR) and deciding whether it meets the 4:1 target, building a measurement uncertainty budget (Type A/Type B, GUM-style) for a calibration, tracing a reference standard's chain of custody to NIST/SI, setting or adjusting a calibration interval from in-tolerance/out-of-tolerance history, or writing the accept/reject call and guard-banded pass/fail on a calibration certificate.

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Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

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• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

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• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

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Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

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Build probability estimates from historical frequencies and supporting or opposing evidence. Structure forecasts with explicit assumptions and outcome criteria, update them as evidence changes, and assess past predictions using accuracy and confidence calibration.

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• Estimating probabilities for technology or economic developments
• Weighing evidence for and against a forecast
• Updating forecast probabilities when new evidence appears

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

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[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current. Use when the user says: model-optimization, model optimization, optimize for model, onboard new model, calibrate new model, new model calibration, model calibration.

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Daily triage accuracy calibration — use during scheduled calibration runs to verify triage classification accuracy against few-shot examples and adjust confidence thresholds

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Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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Playbook for correcting a named skill's star level to match its computed Trust Magnitude grade, with an independent post-calibration verification pass and a CLI-logged timeline event (never hand-written). Use when: recalibrating a skill's level, fixing a stale or drifted star rating, running a batch of `gaia dev calibrate` corrections, or confirming a past calibration actually landed. Triggers: "calibrate <skill>", "recalibrate the trust magnitude backlog", "promote/demote <skill> to N stars", "why is <skill> still N stars", "verify a calibration actually landed", "Hall of Heroes ranking is wrong".

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Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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Assesses whether a patient performs self-monitoring blood glucose (SMBG) fingerstick calibrations as required for their real-time CGM system. Triggers include: 'Verify patient performs fingerstick calibrations', 'Ensure calibration schedule is followed'.

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Learns a user's personal writing and speaking style through interactive writing prompts, then generates a reusable voice profile. Use when the user says "learn my writing style," "write like me," "capture my voice," "voice calibration," "mimic my tone," "my writing style," or wants AI to match their personal communication style. Also activates when a .voice-profile.md file exists and the user asks you to write something in their voice.

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Simulate peer review through five perspectives covering journal fit, methods, subject expertise, interdisciplinary implications, and counterarguments. Receive separate review reports, an editorial decision, and a revision roadmap, or check whether revisions address earlier comments.

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• Simulate peer review before journal submission
• Check research methods and statistical validity
• Test core arguments against counterarguments

Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

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• Planning analytical validation studies and acceptance criteria
• Evaluating calibration, accuracy, and precision
• Estimating and confirming detection and quantitation limits

Prepare self-assessments, manager reviews, and documents for meetings that align employee ratings. Structure achievements, goal evidence, development plans, and promotion cases, turning vague feedback into specific observations about behavior and impact.

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• Prepare a self-assessment for a review cycle
• Write a manager review for a direct report
• Prepare team rating and promotion discussions

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

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• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Update individual settings in an existing product counsel profile, such as risk thresholds, escalation contacts, launch review categories, or marketing claims rules. The guided process explains downstream effects, checks inconsistencies, and saves confirmed changes without repeating setup.

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• Adjust risk thresholds used in product legal reviews
• Update escalation contacts and product partners
• Add a category to the launch review framework

Run and operate the DeepStream Multi-View 3D Tracking reference app, also known as MV3DT. Use when the user asks to set up prerequisites, run shipped MV3DT samples, run Multi-View 3D Tracking on custom synchronized MP4 datasets, import camera calibration, delegate missing calibration to AutoMagicCalib, inspect OSD or BEV visualization, consume MV3DT Kafka metadata, or clean up MV3DT run state in the DeepStream MV3DT app directory.

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Runs performance systems that change behavior — expectations, feedback, review cycles, calibration, and handling underperformance. Use this to design or fix a review process, run calibration, write or coach on feedback, address sustained underperformance, or work out why reviews consume weeks and change nothing.

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Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.

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Customize A2L (ASAP2) files generated from Simulink models using coder.asap2 and coder.mapping APIs. Use this skill when adding COMPU_METHODs, creating GROUPs, converting STD_AXIS to COM_AXIS, adding BIT_OPERATIONs, creating VARIANT_CODING, excluding measurements or struct elements, removing DEFAULT_EVENT_LIST, or any A2L customization for calibration tool compatibility (INCA, CANape). Also use when the user mentions ASAP2, ECU calibration data, A2L export.

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