An EffectiveMulti-Layer Model for Controlling the Quality of Data
Bibliographic record
Abstract
Data mining aims to search for implicit, previously unknown, and potentially useful information that might be embedded in the data. It is well known that in, garbage out. Hence, to get meaningful mining results, a clean set of data is essential. In this paper, we propose an effective model for controlling the quality of data. Specifically, this three-layer model focuses on data validity and data consistency. To elaborate, the internal layer ensures that the observed data are valid and their values fall within reasonable ranges. The temporal layer ensures that data are consistent with their temporal behaviour. The spatial layer ensures that data are consistent with their spatial neighbours. A case study on applying our proposed model to real-life weather data for an agricultural application shows that our model is effective in controlling and improving data quality, and thus leading to better mining results. It is important to note the application of our proposed model is not confined to the weather data for agricultural applications. We also discuss, in this paper, how the proposed three-layer model can be effectively applicable to control the quality of data in some other real-life situations.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".