The Effect of Query Characteristics on Retrieval Results in the TREC Retrieval Tests
Bibliographic record
Abstract
There have been three Text Retrieval Conferences (TREC) organized by the National Institute of Standards and Technology (NIST) over the last three years which have compared retrieval results on fairly large databases (at least 1 gigabyte). The queries (called topics), relevance judgements and databases were all provided by NIST. The main goal of the tests was to compare various retrieval algorithms using various measures of retrieval effectiveness. When Tague-Sutcliffe (in press) performed an analysis of variance on the average precision there is a large group of systems at the top of the ranking which are not significantly different. In addition the queries contribute more to the mean square than the systems. To gather further insight into the results, this research investigates the variation in query properties as a partial explanation for the variation in retrieval scores. For each topic statement for the queries, the length (number of content words), length of various parts and total number of relevant documents are correlated with the average precision.
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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 | 0.060 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".