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Record W2401796064

ClueWeb09 and TREC Diversity.

2010· article· en· W2401796064 on OpenAlex
Charles L. A. Clarke

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNTCIR · 2010
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceInformation retrievalTask (project management)World Wide WebSet (abstract data type)Search engineWeb page
DOInot available

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.226
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it