A data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis
Bibliographic record
Abstract
Abstract Despite rapid growth in continuous monitoring of dissolved oxygen for lake metabolism studies, the current best practice still relies on visual assessment and manual data filtering of sensor observations by experienced scientists in order to achieve meaningful results. This time consuming approach is fraught with potential for inconsistency and individual subjectivity. An automated method to assure the quality of data for the purpose of metabolism modeling is clearly needed to obtain consistent results representative of collective expertise. We used a hybrid approach of expert panel and data mining for data filtration. Symbolic Aggregate approXimation (SAX) treats discretized numerical timeseries segments as symbolic indications, creating a series of strings which are literally comparable to human words and sentences. This conversion allows established text mining techniques, such as classification methods to be applied to timeseries data. Half‐hourly frequency surface dissolved oxygen data from 18 global lakes were used to create day‐long segments of the original time series data. Three hundred sets of 1‐d measurements were provided to a group of seven anonymous experts, experienced in manual filtering of oxygen data for metabolism modeling studies. The collective results were treated as expert panel decisions, and were used to rank the data by confidence level for use in metabolism calculations. While considerable variation occurred in the way the experts perceived the quality of the data, the model provides an objective and quantitative assessment method. The program output will assist the decision making process in determining whether data should be used for metabolism calculations. An R version of the program is available for download.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".