Machine learning-guided field site selection for river classification
Bibliographic record
Abstract
• AI can help experts locate best spots to sample nature. • Human–in–the–loop ML framework optimizes reach-scale field site selection. • High-uncertainty field sites capture previously unrecognized stream types. • Replacement method preserves geomorphic characteristics for inaccessible sites. Sufficient abundance and variety of field site sampling are crucial for obtaining an accurate reach-scale river classification of a regional stream network in support of scientific research and river management. However, many studies still randomly select field sites or only visit accessible streams. This leads to an inadequate exploration of stream characteristics, resulting in incomplete or inaccurate classification. Machine learning has been recognized for discovering and extracting streams’ geomorphic patterns efficiently and accurately from data, but its application in field site sampling design is still in its infancy. This study developed a general and practical field site selection framework by incorporating machine learning in a human-in-the-loop manner. This framework includes three steps: (1) initial field site selection via machine learning from prior datasets, (2) selected field site accessibility evaluation and observation, and (3) additional field site decision and selection via an iterative learning process. In an example application to the San Francisco Bay Area (California, USA), our framework extracted representative geomorphic characteristics of (i) previous known stream types from prior labeled and geospatial datasets and (ii) previously unrecognized stream types based on uncertainty information obtained by machine learning. Moreover, we propose methods for replacing inaccessible sites to ensure sufficient information is retained in the selected field sites. Results revealed clear differences in variable distributions between the 148 high‐certainty sites and the 51 high‐uncertainty sites, a pattern that was validated by our field surveys. Furthermore, the 41 newly identified high‐uncertainty sites were found under-represented in the initial surveyed sites and thus their selection for the next round of field surveys will help fill the important feature gaps left by the initial survey. The feasibility of this framework allows river scientists and land use decision-makers to better understand river patterns and manage spatial planning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".