Having a ChuQL at XML on the Cloud.
Bibliographic record
Abstract
Abstract. MapReduce/Hadoop has gained acceptance as a framework to process, transform, integrate, and analyze massive amounts of Web data on the Cloud. The MapReduce model (simple, fault tolerant, data parallelism on elastic clouds of commodity servers) is also attractive for processing enterprise and scientific data. Despite XML ubiquity, there is yet little support for XML processing on top of MapReduce. In this paper, we describe ChuQL, a MapReduce extension to XQuery, with its corresponding Hadoop implementation. The ChuQL language incorporates records to support the key/value data model of MapReduce, leverages higher-order functions to provide clean semantics, and exploits side-effects to fully expose to XQuery developers the Hadoop framework. The ChuQL implementation distributes computation to multiple XQuery engines, providing developers with an expressive language to describe tasks over big data. 1
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".