A Data Discovery and Visualization Tool for Visual Analytics of Time Series in Digital Agriculture
Bibliographic record
Abstract
In the current era of big data, huge volumes of data can be easily generated and collected at a high velocity from a wide variety of rich data sources. Embedded in these big data-which may also contain many labels or tags-are implicit, previously unknown and potential useful information that can be discovered. Discovered knowledge helps user get a better understanding of the data. However, amounts of discovered knowledge from these huge volumes of big data can also be large. To help users comprehend the discovered knowledge, visualization approaches are in demand. In this paper, we present a data discovery and visualization tool. The tool enables users to visually monitor and explore multi-sourced, multi-tagged time-series data. It also enables users to conduct visual analytics to discover interesting data/knowledge and to visualize this information. Although we demonstrate the practicality of our tool for multi-sourced, multi-tagged time-series data from the agricultural sector, our tool can be applicable to a wide variety of other domains.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".