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机器辅助翻译草稿 (Chinese) for "Tool Call Human Approval": Tool Call Human Approval is a ai control step that requires a person to approve sensitive or high-impact actions for model-triggered calls into software systems. It uses risk scoring, review UI, and audit logs so teams can keep protected decisions accountable while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used Tool Call Human Approval when the assistant requested a protected operation, so the team could keep protected decisions accountable before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Training Model Card": Training Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for model learning and optimization workflows. It uses dataset notes, metric tables, and risk statements so teams can publish model behavior honestly while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Training Model Card when the training job restarted, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Vector Provenance Ledger": Vector Provenance Ledger is a ml record that tracks where data came from and how it changed for numeric representation and similarity search. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Vector Provenance Ledger when the vector store returned close matches, so the team could audit model inputs reliably before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Routing Context Contract": Routing Context Contract is a ai interface contract that defines what context may be passed into a model call for selection among models, tools, and workflows. It uses schemas, redaction rules, source labels, and token budgets so teams can keep model inputs relevant and safe while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used Routing Context Contract when the router selected a cheaper model, so the team could keep model inputs relevant and safe before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Read URL Signal": The Read URL Signal is a ranking or context signal that describes the read url inside a PlatPhorm News article listing. It lets humans and agents scan stories quickly, compare sources, and choose whether to read the article or open its discussion.

“示例草稿: The Read URL Signal helped the reader understand the article listing before opening the full story.”

机器辅助翻译草稿 (Chinese) for "Model Drift Label Review": Model Drift Label Review is a ml quality workflow that checks annotations for consistency and usefulness for changes in model performance over time. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Model Drift Label Review when the live population changed, so the team could improve supervised learning data before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Observability Rollback Plan": Observability Rollback Plan is a devops recovery plan that defines how to return to a known good version for logs, metrics, traces, and events. It uses version pins, database notes, and operator steps so teams can recover quickly from bad changes while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The DevOps team used Observability Rollback Plan when latency increased after deploy, so the team could recover quickly from bad changes before the deployment window opened.”

机器辅助翻译草稿 (Chinese) for "Training Evaluation Harness": Training Evaluation Harness is a ml test system that runs repeatable checks against model behavior for model learning and optimization workflows. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Training Evaluation Harness when the training job restarted, so the team could compare releases with evidence before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Training Feature Store": Training Feature Store is a ml service that serves consistent features to training and inference for model learning and optimization workflows. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Training Feature Store when the training job restarted, so the team could avoid training-serving skew before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Posted Age Signal": The Posted Age Signal is a ranking or context signal that describes the posted age inside a PlatPhorm News article listing. It lets humans and agents scan stories quickly, compare sources, and choose whether to read the article or open its discussion.

“示例草稿: The Posted Age Signal helped the reader understand the article listing before opening the full story.”

机器辅助翻译草稿 (Chinese) for "Service Mesh Resolver Cache": Service Mesh Resolver Cache is a networking performance layer that stores DNS answers for reuse until they expire for east-west service communication. It uses TTL rules, cache keys, and invalidation so teams can reduce lookup latency while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The network engineering team used Service Mesh Resolver Cache when a service called another service, so the team could reduce lookup latency before traffic crossed a service boundary.”

机器辅助翻译草稿 (Chinese) for "Mission Control Fault Detection": Mission Control Fault Detection is a space control that finds off-nominal behavior before it becomes a mission-impacting failure for flight control room coordination. It uses telemetry thresholds, trend checks, and operator review so teams can choose a safe response while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The mission team used Mission Control Fault Detection when the operations console detected a constraint, so the team could choose a safe response before the next mission decision point.”

机器辅助翻译草稿 (Chinese) for "Systems Thinking Aspect: Think in terms of relationships": A practice note from the Systems Thinking principle: Think in terms of relationships, not just components.

“示例草稿: The learner used this systems thinking aspect to make their study plan more polymathic.”

机器辅助翻译草稿 (Chinese) for "Propulsion Link Budget": Propulsion Link Budget is a space planning model that estimates whether a signal path has enough margin for reliable communication for thruster, burn, and maneuver systems. It uses antenna gain, path loss, modulation, and noise estimates so teams can schedule contacts with realistic margins while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The mission team used Propulsion Link Budget when the burn plan changed, so the team could schedule contacts with realistic margins before the next mission decision point.”

机器辅助翻译草稿 (Chinese) for "Serverless Checkpoint Restore": Serverless Checkpoint Restore is a compute recovery workflow that resumes work from a saved state for event-driven function execution. It uses snapshots, state files, and integrity checks so teams can recover long-running work while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Serverless Checkpoint Restore when the function received a traffic burst, so the team could recover long-running work before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Serverless Image Hardening": Serverless Image Hardening is a compute security practice that reduces risk inside packaged runtime images for event-driven function execution. It uses minimal bases, patching, and vulnerability checks so teams can ship safer workloads while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Serverless Image Hardening when the function received a traffic burst, so the team could ship safer workloads before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Navigation Trajectory Correction": Navigation Trajectory Correction is a space maneuver process that adjusts a planned flight path after navigation updates or mission changes for position, timing, and trajectory services. It uses delta-v estimates, burn timing, and post-maneuver validation so teams can reduce path error before it grows while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The mission team used Navigation Trajectory Correction when the navigation solution was updated, so the team could reduce path error before it grows before the next mission decision point.”

机器辅助翻译草稿 (Chinese) for "Ground Station Trajectory Correction": Ground Station Trajectory Correction is a space maneuver process that adjusts a planned flight path after navigation updates or mission changes for antenna, scheduling, and downlink operations. It uses delta-v estimates, burn timing, and post-maneuver validation so teams can reduce path error before it grows while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The mission team used Ground Station Trajectory Correction when the antenna handoff began, so the team could reduce path error before it grows before the next mission decision point.”

机器辅助翻译草稿 (Chinese) for "Experiment Training Checkpoint": Experiment Training Checkpoint is a ml recovery artifact that saves model state during learning for controlled model comparison. It uses weights, optimizer state, and run metadata so teams can resume or inspect training safely while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Experiment Training Checkpoint when the experiment showed a metric tradeoff, so the team could resume or inspect training safely before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Evaluation Grounding Check": Evaluation Grounding Check is a ai quality control that verifies that generated answers are backed by available sources for AI quality and safety testing. It uses citation checks, retrieval evidence, and contradiction detection so teams can reduce unsupported claims while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used Evaluation Grounding Check when a release candidate failed a reasoning scenario, so the team could reduce unsupported claims before the agent workflow reached production.”