The use of digital video recorders in pollination biology
Bibliographic record
Abstract
1. Digital video recording ( DVR ) devices, such as the GoPro H ero, have the potential to greatly benefit pollination ecology, but the advantages of digitally recording pollinator activity over direct human observation have not been formally assessed. 2. Two plant taxa, L avandula angustifolia and C anna ‘sp.’, with differing floral morphology, were used to compare the value of DVR and direct observations in estimating honeybee ( A pis mellifera ) visitation, flower density and number of flowers visited per foraging bout. 3. The two methods yielded identical results when observing the structurally simple L . angustifolia at both high (10.54 ± 0.52 per plant) and low honeybee density (2.24 ± 0.20 per plant). However, DVR underestimated the number of flowers scored in the field of view (28.7 ± 1.8 direct vs. 22.7 ± 0.9 DVR ), the number of honeybees observed (5.3 ± 0.8 direct vs. 3.7 ± 0.7 DVR ) and the number of flowers visited during foraging bouts (8.3 ± 1.2 direct vs. 5.5 ± 1.0 DVR ) on the more complex C anna ‘sp.’ 4. It is concluded that portable weatherproof DVR devices such as the GoPro H ero are valuable tools for pollination biologists, allowing a single researcher to make simultaneous observations of multiple plants in one or more sites, whilst also allowing the footage to be reviewed. However, DVR devices are limited by their depth and field of view when target plants are large or structurally complex.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".