High-resolution, low-altitude aerial photography in physical geography
Bibliographic record
Abstract
Intertidal landscapes are highly complex and dynamic habitats that exhibit variability over a range of spatial and temporal scales. The spatial arrangement of structure-forming biogenic features such as seagrasses and bivalves influences ecosystem function and the provision of important ecosystem services, though quantification and monitoring of intertidal landscape structure has been hindered by challenges collecting spatial data in the coastal zone. In this study, an intertidal landscape mosaic of eelgrass ( Zostera marina) and blue mussels ( Mytilus edulis) was observed using low-altitude aerial photography from a balloon-mounted digital camera platform. Imagery representing seagrass-bivalve landscape structure was classified and analysed using multiple metrics of landscape composition and configuration at the patch scale and the landscape scale. Patch-scale imagery was compared to a previously collected dataset in order to track temporal changes in seagrass patch metrics over a 26-month period. Seagrass and bivalve patches exhibited distinct spatial patterning at different spatial scales. At the patch scale, the change in seagrass metrics was consistent with patch border expansion at the expense of patch density and integrity. These methods demonstrate a novel approach for collecting high-resolution spatial data that could also be valuable to physical geographers dealing with similar fine-scale landscapes. The application of spatial metrics at multiple spatial scales quantified elements of the configuration and composition of a seagrass-bivalve habitat mosaic and allowed for the tracking of patch metrics through time to depict landscape change. Continued development of landscape metrics within intertidal habitats will increase understanding of the ecological function of these areas with benefits to management and monitoring of ecosystem health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".