Skill UI

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

This comprehensive guide outlines the scope, required evidence, and structural standards for submitting mechanistic immunology research to Science Immunology (Sci Immunol). It details how to position studies with translational relevance, emphasizing the combination of human clinical data with in vivo mouse models. It serves as a critical tool for authors to assess the manuscript's fit, adherence to AAAS standards, and overall scientific rigor before submission.

This guide details the unique two-deadline review model of SenSys. It provides authors with a comprehensive roadmap for interpreting complex rejection outcomes, structuring substantive revisions, formulating effective responses to reviewers, and understanding critical systems review criteria, such as energy measurement and baseline fairness. Essential reading for systems researchers.

This guide details the expected review standards and process for submitting academic manuscripts to general social-science journals, referencing the Social Forces review model. It covers double-anonymization rules, required methodological rigor, achieving general significance, structuring theoretical arguments, and anticipating typical reviewer critiques. Use this before submission to stress-test your work and shape it for a higher chance of acceptance.

A comprehensive guide for ensuring the reproducibility of Information Retrieval (IR) research, essential for SIGIR submissions. It details best practices for pinning data collection versions, index settings, neural model hyperparameters, and managing non-determinism. It helps researchers document artifacts—not just promises—to validate reported gains.

A detailed guide modeling the complex academic review machinery, using SIGIR as a primary example. It covers everything from per-track anonymity regimes and PC-member nomination duties to the critical scoring criteria: evaluating validity, fairness of comparison, and statistical rigor. Essential reading for authors preparing high-stakes research submissions.

This guide details the critical 'triage protocol' for structuring academic papers for highly constrained venues like SIGIR. It outlines the three-container model (Paper Body, References, Repository) and provides expert advice on prioritizing review-critical evidence, implementing effective content compression techniques, and managing external repository material to ensure submissions meet strict page budgets without sacrificing soundness or review quality. Essential reading for researchers.

A detailed guide on the complex, hybrid review process for ACM SIGMETRICS submissions. It outlines the implications of the three distinct first-round outcomes (Accept, One-Shot Revision, Reject), the highly rigorous evaluation criteria (proofs, models, assumptions), and crucial procedural rules, such as the single-shot revision window and the 12-month resubmission bar. Essential reading for authors navigating academic publication.

A comprehensive guide for authors revising performance-intensive academic papers, such as those for SIGMETRICS. It emphasizes the need to state a precise performance contribution on the first page, explicitly detail all modeling assumptions, and pair every quantitative claim with proportional evidence (proofs, simulations, and measurements).

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

This guide details the rigorous academic review process for ACM Symposium on Cloud Computing (SoCC) submissions. It explains the dual-anonymous, two-round review structure, the importance of incorporating both systems performance and data measurement rigor (including tail latency and cost), and how the rebuttal period functions. Essential reading for authors aiming to publish in top-tier cloud computing venues.

This comprehensive guide provides strict structural and technical guidelines for authors submitting papers to ACM SoCC. It mandates focusing on cloud-scale problems recognized by operators, emphasizing contributions at the intersection of systems and data. Key requirements include reporting tail latency (p95/p99) and concrete cost models, grounding every claim in measured, real-world evidence, and explicitly stating external limitations.