Program
Bibliographic record
Abstract
meta-learning.Specifically, we will introduce three lines of meta-learning methods, i.e., gradient-based methods, metrics-based methods, and memory-based methods.Furthermore, we will also present the application of meta-learning in real-world systems such as smart grids and transportation systems.Finally, we will discuss the challenges and potential research directions. T2: Tutorial 2 -Introduction to Codes and StandardsNehad El-Sherif Room: Harbour Suites B Codes and Standards are indispensable because of the essential role they play in our lives.They touch every aspect of our lives by ensuring safety, quality and reliability of products and services.Additionally, codes and standards guarantee compatibility between different markets to facilitate international trade.According to Raymond G. Kammer, the past director of the US National Institute of Standards and Technology (NIST), about 80% of global merchandise trade is affected by standards and by regulations that embody standards.Therefore, it is important to understand the standards development process, the different types of standards, and how to become actively involved in the development process.This tutorial provides attendees having little or no background of codes and standards with the information required to develop a basic understanding of the topic.It also serves as a refresher for experienced attendees.The 5W's method will be used to engage attendees and historical examples to emphasize the importance of complying with codes and standards.Innovation and its relation to standards will be discussed.Finally, a real-life design example will be shared with attendees to demonstrate how codes and standards are applied in practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.232 | 0.097 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".