INFX 2600 – Text Matching, Statistics, Web Services, and Swine Flu
Bibliographic record
Abstract
So the purpose of this lecture is to give you an overview of the things I will be teaching you in this semester and the next. Rather than go into horrific detail about the topics, my intention today (with Norm's help) is to give you one example of why the topics I cover are relevant. My take on the Informatics program, and especially INFX2600, is that it is about the acquisition, handling, analysis, interpretation, and reporting of data. This encompasses a lot of the scripting, database and Web material you will be learning (including PHP and client-server architectures), but also includes managing and manipulating files, extracting data, and looking for significant patterns and trends in those data. My lectures in November will cover some technical matters including the power of UNIX commands, extracting patterns from text (with a focus on regular expressions), and a theoretical concept called finite state machines which can be used to represent and implement regular expressions, but can be used to represent many other processes as well. In the Winter semester, I will introduce you to the use of statistics for data analysis, including descriptive (what is the average house price in Halifax?) and inferential (does playing Gran Turismo make you a better driver?) statistical analysis. 1 But these things are all in the future. For now, we will focus on one "application domain " that is of relevance to my lab and of passing interest to others too: the tracking and analysis of the 2009 Influenza A H1N1 (also
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.060 |
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".