Comparing methods for controlled capture and quantification of pollen in <i>Cannabis sativa</i>
Bibliographic record
Abstract
Premise Precise pollen collection methods are necessary for crop breeding, but anemophilous pollen is notoriously difficult to capture and control. Here we compared a variety of methods for the controlled capture of cannabis pollen, intended to ease the process of cross‐fertilization for breeding this wind‐pollinated plant, and measured the utility of light spectroscopy for quantifying relative pollen yield. Methods and Results In two independent trials, we compared a control method of pollen collection (hand collection) to either vacuum‐, water‐, or bag‐collection methods. We used visible light spectroscopy to quantify relative pollen yield, and validated this approach using microscopic pollen counts. We determined that pollen yield was highest when using hand collection or vacuum collection, but efficiency did not differ significantly among methods. Conclusions To maximize yield, pollen should be collected by hand or vacuum, but all collection methods were equally efficient in a relative sense because yield increased with collection time. We also found that light spectroscopy is an accurate and rapid method of quantifying pollen abundance ( R 2 = 0.86) in a liquid suspension.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".