Long-term time-lapse video provides near complete records of floral visitation
Bibliographic record
Abstract
Accurate records of floral visitors are critical for understanding plant pollinator interactions. However, to date, sampling methods are constrained to short sampling periods and may be subject to observer interference. Thus, complete records without sampling bias are rare. We use a portable time-lapse digital video camera to capture near-complete records of visitors to flowers over their entire blooming period. We show the broad applicability of this method by filming a wide variety of flowers of different shapes and inflorescence types. We test the importance of long-term records by studying visitors to Cornus canadensis (bunchberry dogwood). Visitors to C. canadensis filmed simultaneously at four different sites show variation (both in rates and taxa) between inflorescences, between sites, throughout the day, and throughout the season. For C. canadensis our films also provide a record of pollen placement (an indirect measure of male fitness) and fruit set (female fitness). This technique provides near complete records of floral visitors, is likely to capture rare events, and allows simultaneous long-term filming. These results emphasize the importance of both long-term data collection and simultaneous recording at multiple sites for pollination studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".