Elastic and inelastic LiDAR pulse return phenomenology in coastal underwater biological substrates
Bibliographic record
Abstract
In the context of current and future climate-related environmental changes, the development of innovative underwater substrate detection, classification and imaging methods at large spatial scales is key in monitoring and understanding changes from stresses occurring in coastal ocean areas. This development will help understand the spatial distribution and abundance patterns of marine primary producers and ecosystem service providers such as macroalgae, eelgrass and other important ecosystem components such as coral, and can provide insights into future ecosystem response and better management practices. The objective of the current work is to describe an analysis of data acquired by full waveform underwater fluorescence LiDAR, designed for detecting, imaging, and generating 3D point clouds of inert and biological substrates capable of fluorescence. Since the instrument is designed as a small form-factor AUV payload operating at standoff distances of 5–10 m, we chose to implement full-waveform (2.5 Gs/s), pulsed 532 nm laser, capable of generating 1 ns pulses of up to 2.5 uJ at a 200 kHz repetition rate to generate elastic (532 nm) and inelastic (685 nm) 3D point clouds for underwater benthic mapping. Analysis of these acquired waveforms has shown opportunities for improving the point cloud density, by identifying multiple returns within the same waveform, when present. Pulse return processing methods such as Gaussian decomposition and Richardson-Lucy deconvolution are evaluated on data acquired during LiDAR sea-trials over various bottom substrates. As the present LiDAR beam footprint is relatively small to maximize energy density for longer range detection and potential fluorescence response, the number of detected returns per pulse ranges from one in the case of a bare benthic substrate and up to 2 or 3, in areas where for example, macroalgae, kelp, corals and/or other substrates characterized by a vertical structure are present.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".