Drone monitoring of volcanic lakes in Costa Rica: a new approach
Bibliographic record
Abstract
For the first time ever, samples were collected from volcanic lake waters in Costa Rica using an unmanned aerial vehicle (drone), which represents a major achievement in human–machine interaction and innovation in the technology sector. A Matrice 600 Pro drone was used for remote sampling in the hyperacid crater lake of the Poás volcano, the mildly acidic Lake Botos, and the nearly neutral Lake Hule. A bailer bottle of 250 mL and a HOBO temperature probe, mounted on the drone, were deployed using a specially designed delivery retrieval system. A comparison was carried out relating to the geochemistry of lake water collected by drone as opposed to the hand-collected samples. The SO4−2/Cl ratios of the two samples at Poás hyperacid crater lake were similar, (1.1 ± 0.2) on average, an indication of a lake with homogenous water composition. The Lake Hule showed a similar composition to that registered 20 years ago. The waters from Lake Botos showed some differences, which may be explained by the influence of springs at the bottom of the lake, but the Wilcoxon's signed-rank test showed a good exhibit of a satisfactory level of similarity. Autonomous navigation proves to be very useful for faster, more efficient, reliable, and less hazardous sampling of volcanic lakes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".