APPLICATIONS OF PHOTOGRAMMETRY TO NEOICHNOLOGICAL STUDIES: THE SIGNIFICANCE OF SHOREBIRD TRACKWAY DISTRIBUTIONS AT THE BAY OF FUNDY
Bibliographic record
Abstract
Abstract Low-level drone photogrammetry is a technique that allows for the construction of surface orthomosaics and elevation models. Despite being used for a wide range of geological applications, these types of datasets have not yet been explored from a neoichnological perspective. This study uses three examples of tidal flats from the Bay of Fundy to demonstrate the usefulness of 3D photogrammetry in the collection of high-resolution neoichnological datasets. The first site is a bar top along the Petitcodiac River that is situated between a salt marsh and tidal channel margin. The second site, which is located along the Shepody River, represents a laterally accreting channel margin and thus has a relatively high relief. In contrast, the final site comprises a gently sloping tidal flat that is far from the influence of a major tidal channel. Spatial analysis of the shorebird tracks at each site was used to assess the relationship between shorebird track distributions (e.g., track density, stride orientation, stride length) and various environmental and ecological factors (e.g., topography, tidal current direction, invertebrate prey distributions). Additionally, morphological analysis of the tracks was used to assess variations in substrate cohesiveness, which is the environmental factor that exhibited the most variability at the study locations. The track record at each site represents a shorebird flock that traversed the tidal flats in such a way as to optimize foraging success. As a result, the abundance and availability of macrobenthic invertebrate prey was the primary factor contributing to shorebird track density. This paper aims to assess local-scale variations in tidal flat substrate cohesiveness and provide context for the interpretation of fossilized shorebird trackways.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".