Ancillary data from animal-borne cameras as an ecological survey tool for marine communities
Bibliographic record
Abstract
Abstract Underwater visual surveys represent an essential component of coastal marine research and play a crucial role in supporting the management of marine systems. However, logistical and financial considerations can limit the availability of survey data in some systems. While biologging camera tag devices are being attached to an increasing diversity of marine animals to collect behavioral information about the focal species, the ancillary imagery collected can also be used in analytical techniques developed for diver-based surveys. We illustrate this approach by extracting ancillary data from shark-borne camera tag deployments focused on the behavior of a White shark (Carcharodon carcharias) off Gansbaai, South Africa, and a Grey Reef shark (Carcharhinus amblyrhynchos) within the Chagos Archipelago. Within the giant kelp forest environment of Gansbaai we could determine the spatial density of kelp thali and underlying substrate composition. Within the coral reef environment, the animal-borne video allowed us to determine the approximate percent and type of benthic cover, as well as growth form and genus of corals down to the upper mesophotic zone. We also enumerated fish species-level abundance over reef flat and wall environments. We used established dive-survey methods to analyze video data and found the results to be broadly comparable in the two systems studied. Our work illustrates the broad applicability of ancillary animal-borne video data, which is analogous in type and quality to diver-based video data, for analysis in established marine community survey frameworks. As camera tags and associated biologging technologies continue to develop and are adapted to new environments, utilising these data could have wide-ranging applications and could maximise the overall cost–benefit ratio within biologging deployments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".