High‐resolution remote sensing of intertidal ecosystems: A low‐cost technique to link scale‐dependent patterns and processes
Bibliographic record
Abstract
Linking experimentally tested local processes to natural patterns in intertidal ecosystems requires data‐acquisition techniques that provide spatiotemporal data from the scale of local processes to the scale of patterns. We developed a low‐cost, high‐resolution remote‐sensing technique based on the use of a 6‐m helium‐inflated blimp, a standard 35‐mm camera, and photogrammetric numerical tools in order to acquire high‐resolution data of environmental (i.e.,\ topographic) and biological (i.e., algal biomass) variables over intertidal landscapes. The camera was calibrated for photogrammetric analysis, and overlapping color aerial photographs were taken at an altitude of 80 and 50 m. We performed stereo analysis of digitized images and numeric topographic restitution over an 18 3 18 m area with an error of 0.02 m along the Z axis. A normalized vegetation index (NDVI) from color‐infrared images at 0.02‐m resolution over the same area was computed. Algal biomass sampled within the photographed area allowed us to calibrate NDVI with algal biomass (R2 = 0.73, p < 0.01). Aggregation analysis performed on a height above zero level, local topographic heterogeneity, and algal biomass confirmed, at the landscape level, previous local experi‐mental evidence of a relationship between topographic heterogeneity and algal biomass increasing from scales of 0.5 to 2 m. Our method permits multiscale testing of local scale‐dependent processes over a natural landscape.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".