“experiment”的搜索结果

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

中文简介正在准备中。当前显示原文说明。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

中文简介正在准备中。当前显示原文说明。

Chaos Engineering discipline — proactive experimentation on distributed systems to build confidence in their resilience. Covers Netflix-inspired chaos principles, experiment design, blast radius control, LitmusChaos and Chaos Mesh usage, game day planning, and production safety. USE WHEN: validating system resilience before production incidents, designing chaos experiments, planning game days, implementing CI/CD chaos pipeline, or building confidence in fault tolerance. Triggers on "chaos engineering", "chaos experiment", "game day", "resilience test", "fault injection", "Litmus", "Chaos Mesh", "break things on purpose".

中文简介正在准备中。当前显示原文说明。

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

中文简介正在准备中。当前显示原文说明。

协助撰写机器学习与AI研究论文，涵盖文献检索、实验设计、运行进度检查、结果分析、写作和投稿准备，并核验引用、处理审稿后的修改。

• 根据已有代码和结果撰写论文
• 规划支撑研究主张的实验
• 检查相关研究和引用

在正式开发前，用一次性原型验证想法是否可行。协助整理疑问、调查方案、运行实验、比较方法，并记录结论、依据与限制，为下一步验证提供参考。

• 验证想法是否可行
• 通过原型比较不同库
• 开发前检验高风险假设

将访谈、客服工单、问卷和使用记录整理为用于产品或设计决策的HTML页面，集中展示证据、主题、可信程度、改进方案比较及下一步实验。

• 综合分析访谈与客服工单
• 比较初始设置的改进方案
• 向相关人员分享证据与不确定性

设计和改进SaaS付费套餐升级页面、付费功能访问限制页面及追加购买弹窗，用于规划付费转化界面和价格页面实验。

• 设计付费套餐升级页面
• 改进付费功能访问限制页面
• 设计追加购买提示弹窗

使用fal.ai托管的虚拟试衣模型，通过图像查看服装穿在人身上的效果，适用于电商穿着形象的构思、服装搭配图册和造型方案尝试。

• 构思电商服装穿着展示图
• 尝试搭配图册中的穿搭方案
• 查看服装穿在人身上的效果

用于AI音乐专辑制作，涵盖概念构思、歌词初稿、曲目排序和导出，适合独立专辑实验或品牌音乐企划；完整制作流程需另行安装上游资源包。

• 策划独立制作的专辑
• 围绕专辑主题起草歌词
• 安排专辑曲目顺序

Import a recorded Remotion Canvas Capture as an editable composition with cursor keyframes, without using Studio controls. The workflow generates and registers a component, copies the video asset, and installs missing dependencies through an internal experimental command.

中文简介正在准备中。当前显示英语简介。

• Importing a recording as an editable composition
• Adding a capture without interacting with Studio
• Setting composition dimensions and frame rate during import

Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.

中文简介正在准备中。当前显示英语简介。

• Calculate concentration changes in reaction networks
• Compare baseline and altered kinetic parameters
• Inspect model units and consistency warnings

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

中文简介正在准备中。当前显示英语简介。

• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

中文简介正在准备中。当前显示英语简介。

• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

中文简介正在准备中。当前显示英语简介。

• Plan binding screens for candidate protein sequences
• Estimate experiment costs before submission
• Track laboratory progress and retrieve results

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

中文简介正在准备中。当前显示英语简介。

• Estimate metabolic fluxes from carbon-13 labeling
• Validate atom mappings and measurement inputs
• Simulate labeling for proposed reaction rates

Turn one scientific paper into a structured, 16-section reading card. Break down its methods, connect claims to experimental evidence, identify limitations, and develop research hypotheses, with source references and clear labels for anything the supplied material cannot support.

中文简介正在准备中。当前显示英语简介。

• Break down a paper’s methods and assumptions
• Connect central claims to experimental evidence
• Organize limitations and gaps in supporting evidence

Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.

中文简介正在准备中。当前显示英语简介。

• Investigating failures in LLM applications
• Grouping observed mistakes into failure categories
• Choosing which evaluation tests to build first

Plan a new product launch around finding early customers, guiding their first use, and running short experiments. The skill also helps diagnose stalled adoption, document repeatable outreach methods, and assess whether customer demand supports further expansion.

中文简介正在准备中。当前显示英语简介。

• Finding the first customers for a new product
• Diagnosing weak adoption after launch
• Planning two-week experiments

Run repeated code improvement experiments with multiple agents. It plans and reviews candidate changes, compares benchmark results, merges candidates that pass rechecking, and records progress until a target, iteration limit, or another stopping condition is reached.

中文简介正在准备中。当前显示英语简介。

• Comparing several code improvement ideas with benchmarks
• Running repeated experiments toward a measurable target
• Tracking candidate scores and experiment history

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

中文简介正在准备中。当前显示英语简介。

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.

中文简介正在准备中。当前显示英语简介。

• Comparing signup button copy
• Planning a pricing page experiment
• Estimating sample size and test duration

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

中文简介正在准备中。当前显示英语简介。

• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Create and refine Jupyter notebooks for experiments, exploratory analysis, or tutorials. Bundled templates help organize runnable code and short explanations into clear steps, while validation guidance supports notebooks that others can read and rerun.

中文简介正在准备中。当前显示英语简介。

• Starting a notebook for an experiment
• Turning scripts or notes into a structured notebook
• Creating a step-by-step teaching notebook