In-situ manipulations of aquatic optical depth and its effect on small unoccupied aerial systems–derived spectral reflectance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The collection of spectral data with sensors fixed to various platforms (e.g., satellites, occupied aerial vehicles, and small unoccupied aerial systems (sUAS)) has allowed for the estimation of optically active constituents (OACs) common in surface waters. However, in small, complex, and optically shallow waters where multiple OACs (e.g., chlorophyll-a and total suspended solids) impact the spectral signature, these technologies have experienced significant limitations. Altering the scale at which these examinations are performed on surface waters (e.g., ponds and lakes) to mesocosm systems (37 cm in height and 30 cm in diameter) provides information on the interactions between multiple OACs and insight on the impact aquatic optical depth has on remotely sensed spectra. This field study examines optically shallow and optically deep mesocosm systems simulated in five-gallon buckets to determine the role aquatic optical depth has on developing accurate surface-water quality models. Results demonstrated an accurate representation of OACs in optically deep mesocosms compared with optically shallow mesocosms when assessed with sUAS (i.e., relative percent differences in predicted iron concentrations of −86 and 16 for optically shallow and deep waters, respectively). The interferences observed under these conditions were comparable to literature values when studying optically complex water bodies with hyperspectral data. This study provides a basis for understanding the benefits and limitations of monitoring in-situ water quality via sUAS.
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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 | 0.000 | 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 | 0.000 |
| 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 it