Natural history footage provides new reef fish biodiversity information for a pristine but rarely visited archipelago
Bibliographic record
Abstract
There remain parts of our planet that are seldom visited by humans, let alone scientists. In such locations, crowd-sourced or citizen scientist data can be critical in describing biodiversity and detecting change. Rangitāhua, the Kermadec Islands, are 750 km from the nearest human-habitation. Although our knowledge of this near pristine location has increased with recent biodiversity expeditions, we still lack comprehensive understanding of the marine biodiversity surrounding the islands. In 2015, professional underwater videographers were commissioned to produce a nature documentary focused on Rangitāhua's reefs. We strategically surveyed the raw documentary video and examined how biodiversity estimates differed from traditional scientific surveys. We uncovered three new fish species records for Rangitāhua, extending the known distribution for each species, two of which are also new records for New Zealand waters. Comparison of documentary video footage with scientific survey methods showed that estimates of reef fish species richness from the documentary video were similar to stationary surveys, but lower than non-stationary surveys. Moreover, all survey methods, including documentary video, captured different fish assemblages, reflecting each method's particular bias. Overall, we provide a proof-of-concept for how collaborations between scientists and professional natural historians, such as videographers and photographers, can provide valuable biodiversity information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.007 |
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".