Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The TREC Web Track explores and evaluates Web retrieval technologies. The TREC 2009 Web Track included both a traditional adhoc retrieval task and a new diversity task. The goal of this diversity task is to return a ranked list of pages that together provide complete coverage for a query, while avoiding excessive redundancy in the result list. Both tasks will continue at TREC 2010, which will also include a new Web spam task. The track uses the ClueWeb09 dataset as its document collection. This collection consists of roughly 1 billion web pages in multiple languages, comprising approximately 25TB of uncompressed data crawled from the general Web during January and February 2009. For TREC 2009, topics for the track were created from the logs of a commercial search engine, with the aid of tools developed at Microsoft Research. Given a target query, these tools extracted and analyzed groups of related queries, using co-clicks and other information, to identify clusters of queries that highlight different aspects and interpretations of the target query. These clusters were employed by NIST for topic development. For use by the diversity task, each resulting topic is structured as a representative set of subtopics, each related to a different user need. Documents were judged with respect to the subtopics, as well as with respect to the topic as a whole. In 2009, a total of 18 groups submitted runs to the diversity task. To evaluate these runs, the task used two primary effectiveness measures: -nDCG as defined by Clarke et al. (SIGIR 2008) and an “intent aware” version of precision, based on the work of Agrawal et al. (WSDM 2009). Developing and validating metrics for diversity tasks continues to be a goal of the track. For TREC 2010, we will report a number of additional evaluation measures that have been proposed over the past year, including an intent aware version of the ERR measure described by Chapelle et al. (CIKM 2009). Nick Craswell from Microsoft serves as the track co-coordinator. Ian Soboroff is the NIST contact. The ClueWeb09 collection was created through the efforts of Jamie Callan and Mark Hoy at the Language Technologies Institute, Carnegie Mellon University. More information may be found on the track Web page: http://plg.uwaterloo.ca/~trecweb/2010.html. Bio Charles Clarke is a professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Canada. He has published on a wide range of topics within the area of information retrieval, including papers related to evaluation, efficiency, ranking, parallel systems, security, question answering, document structure, and XML. He was a Program Co-Chair of SIGIR 2007 and General CoChair of SIGIR 2003. From 2004 to 2006 he was the coordinator of the TREC Terabyte Retrieval track. Since 2009 he has been a cocoordinator of the TREC Web Track. He is a co-author of the book Information Retrieval: Implementing and Evaluating Search Engines (MIT Press, 2010). He has previously held software development positions at a number of computer consulting and engineering firms. In 2006 he spent a sabbatical at Microsoft, where he was involved in their search engine development effort.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it