Commodity canola and seed canola visitation and plant data
Bibliographic record
Abstract
Insect-mediated pollination of crops is an important service to agriculture, as increased insect visitation can increase fruit production by increasing pollen deposition. Unfortunately, pollination is often treated as a “black box”, and pollination management suffers from key knowledge gaps that hinder its greater utility, particularly the specific mechanisms underlying the processes of visitation, pollination, and fruit production. We present a causal model that links insect visitation to pollination to three separate components of yield, using field data from two types of canola (Brassica napus) production systems. Our results demonstrate that yield in commodity canola fields is primarily determined by plant size, and we found no relationship between honey bee (Apis mellifera) visitation and pollen deposition, or pollen deposition and seed yield. In contrast, yield in seed production canola fields was similarly controlled by plant size, but there was also a strong relationship between alfalfa leafcutting bee (Megachile rotundata) visitation and pollen deposition, as well as pollen deposition and seed yield. Leafcutting bee visitation in particular strongly increased pollen deposition in seed canola fields, whereas honey bee visitation did not. This model serves as a step towards a dynamic model of pollination services and highlights the relative importance of bee pollination in canola production.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".