Measuring Robustness with First Relevant Score in the TREC 2012 Microblog Track
Bibliographic record
Abstract
Abstract : In this paper, we measure the effectiveness of various experimental search techniques not just with traditional TREC ad hoc search measures such as Average Precision, R-precision and Precision at 30, but also with robust measures based on just the rank of the first relevant item retrieved such as First Relevant Score and Generalized Success at 30. We report the results of our experiments conducted in the context of the Real-Time Adhoc Search Task of the TREC 2012 Microblog Track which investigated the effectiveness of ad hoc search of a collection of more than 10 million tweets. For the experimental technique of favoring tweets with urls, we found that both the traditional and robust measures indicated statistically significant increases in the mean score. However, for an experimental blind feedback technique, a technique known to be non-robust as it typically makes poor results even worse, the traditional Average Precision measure indicated a statistically significant increase in the mean score, but some of the measures just based on the rank of the first relevant item successfully discerned a statistically significant decrease in the mean score from the non-robust technique.
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 | 0.019 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".