Browse A-Z

机器辅助翻译草稿 (Chinese) for "Memory Tool Permission": Memory Tool Permission is a ai access control that decides which tools an AI workflow may call for persistent or session-level AI state. It uses operation allowlists, user intent checks, and protected-action gates so teams can block unsafe automation while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The AI platform team used Memory Tool Permission when the assistant reused earlier project context, so the team could block unsafe automation before the agent workflow reached production.”

机器辅助翻译草稿 (Chinese) for "Memory Workload Priority": Memory Workload Priority is a compute scheduling signal that tells the platform which work matters most when capacity is constrained for volatile runtime storage. It uses priority classes, preemption rules, and fairness limits so teams can protect critical paths while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The platform engineering team used Memory Workload Priority when the process approached its memory limit, so the team could protect critical paths before the workload scaled up.”

机器辅助翻译草稿 (Chinese) for "Metaphysics": Metaphysics is listed by Polymaths as a notable work associated with Aristotle, connecting that figure's public legacy to Philosophy, Science, Logic.

“示例草稿: Metaphysics appears in the Polymaths profile for Aristotle.”

机器辅助翻译草稿 (Chinese) for "Method of Fluxions": Method of Fluxions is listed by Polymaths as a notable work associated with Isaac Newton, connecting that figure's public legacy to Physics, Mathematics, Astronomy.

“示例草稿: Method of Fluxions appears in the Polymaths profile for Isaac Newton.”

机器辅助翻译草稿 (Chinese) for "Metric Bias Audit": Metric Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for measurement of model behavior. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Bias Audit when the metric changed after data cleanup, so the team could surface fairness risks before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Calibration Curve": Metric Calibration Curve is a ml diagnostic that compares predicted confidence with observed outcomes for measurement of model behavior. It uses bucketed predictions, reliability diagrams, and threshold analysis so teams can make confidence scores useful while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Calibration Curve when the metric changed after data cleanup, so the team could make confidence scores useful before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Data Split": Metric Data Split is a ml experimental control that separates examples for training, validation, and testing for measurement of model behavior. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Data Split when the metric changed after data cleanup, so the team could measure generalization honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Drift Monitor": Metric Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for measurement of model behavior. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Drift Monitor when the metric changed after data cleanup, so the team could respond before quality drops before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Embedding Refresh": Metric Embedding Refresh is a ml index workflow that updates vector representations after source data changes for measurement of model behavior. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Embedding Refresh when the metric changed after data cleanup, so the team could keep retrieval results current before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Evaluation Harness": Metric Evaluation Harness is a ml test system that runs repeatable checks against model behavior for measurement of model behavior. 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 Metric Evaluation Harness when the metric changed after data cleanup, so the team could compare releases with evidence before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Feature Store": Metric Feature Store is a ml service that serves consistent features to training and inference for measurement of model behavior. 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 Metric Feature Store when the metric changed after data cleanup, so the team could avoid training-serving skew before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Hyperparameter Sweep": Metric Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for measurement of model behavior. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.

“示例草稿: The machine learning team used Metric Hyperparameter Sweep when the metric changed after data cleanup, so the team could find better configurations before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Label Review": Metric Label Review is a ml quality workflow that checks annotations for consistency and usefulness for measurement of model behavior. 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 Metric Label Review when the metric changed after data cleanup, so the team could improve supervised learning data before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Model Card": Metric Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for measurement of model behavior. 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 Metric Model Card when the metric changed after data cleanup, so the team could publish model behavior honestly before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Provenance Ledger": Metric Provenance Ledger is a ml record that tracks where data came from and how it changed for measurement of model behavior. 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 Metric Provenance Ledger when the metric changed after data cleanup, so the team could audit model inputs reliably before the model moved into evaluation.”

机器辅助翻译草稿 (Chinese) for "Metric Training Checkpoint": Metric Training Checkpoint is a ml recovery artifact that saves model state during learning for measurement of model behavior. 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 Metric Training Checkpoint when the metric changed after data cleanup, 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 "Misinformation Badge": The Misinformation Badge is a visible trust marker that supports trust decisions around misinformation in PlatPhorm News. It helps reviewers and agents evaluate whether article listings, sources, domains, and links should be trusted, warned, or escalated.

“示例草稿: The Misinformation Badge was attached to the listing so reviewers could judge the source before promoting the story.”

机器辅助翻译草稿 (Chinese) for "Misinformation Evidence": The Misinformation Evidence is a supporting record that supports trust decisions around misinformation in PlatPhorm News. It helps reviewers and agents evaluate whether article listings, sources, domains, and links should be trusted, warned, or escalated.

“示例草稿: The Misinformation Evidence was attached to the listing so reviewers could judge the source before promoting the story.”

机器辅助翻译草稿 (Chinese) for "Misinformation Flag": The Misinformation Flag is a review marker that supports trust decisions around misinformation in PlatPhorm News. It helps reviewers and agents evaluate whether article listings, sources, domains, and links should be trusted, warned, or escalated.

“示例草稿: The Misinformation Flag was attached to the listing so reviewers could judge the source before promoting the story.”

机器辅助翻译草稿 (Chinese) for "Misinformation Policy": The Misinformation Policy is a rule set that supports trust decisions around misinformation in PlatPhorm News. It helps reviewers and agents evaluate whether article listings, sources, domains, and links should be trusted, warned, or escalated.

“示例草稿: The Misinformation Policy was attached to the listing so reviewers could judge the source before promoting the story.”