Mapping coastal optical and biogeochemical variability using an autonomous underwater vehicle and a new bio‐optical inversion algorithm
Bibliographic record
Abstract
Autonomous underwater vehicles (AUVs) can map water conditions at high spatial (horizontal and vertical) and temporal resolution, including under cloudy conditions when satellite and airborne remote sensing are not feasible. Applications of this technology are numerous, and harnessing the full potential of AUV platforms for oceanographic research will require innovative sampling and data processing techniques, particularly in shallow littoral environments. We deployed a passive radiometer on a small AUV called the Remote Environmental Monitoring UnitS (REMUS; Hydroid) to demonstrate a novel method that uses one optical sensor at depth to characterize variability in underwater clarity and the constituents of coastal seawater (i.e., dissolved organic material, algal biomass, and other particles). This approach uses spectral differences between attenuation coefficients that are computed from ratios of downwelling irradiance measured at one depth assuming a constant shape, but not magnitude, for the solar irradiance spectrum at the surface. A spectral inversion model provides estimates of the absorption coefficients (m −1 ) of the constituents above the sensor. There is no requirement for sensors at the ocean surface using this approach and, compared to inversion methods for subsurface reflectance, the effects of bottom reflectance in shallow waters are minimal. Maps of biooptical properties at high spatial resolution demonstrate that our approach can be used for characterizing shallow and highly variable coastal waters. This simple and robust technique is also applicable to other in situ sampling platforms (e.g., gliders, moorings, Argo floats).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".