Biotic and abiotic factors contribute to cranberry pollination
Bibliographic record
Abstract
As bee populations continue to decline, farmers face possible crop failures due to insufficient pollination. Crops, however, vary in the degree to which they depend on pollinators, suggesting that some crops may not be as sensitive to variation in pollinator availability and/or abundance as others. The objective of this study was to determine the contribution of biotic and abiotic factors to cranberry pollination. We performed field and greenhouse experiments to compare the effect of biotic (i.e., bee or hand pollination) and abiotic (i.e., wind, agitation) factors on yield. We found that even in the absence of bees, cranberry is able to produce a significant yield. In the field, plants in the abiotic treatments produced higher yields (wind 230 bbl/ac [barrels per acre], agitation 200 bbl/ac) than the closed control treatment (108 bbl/ac), although these yields were not as high as the open, biotic treatment (367 bbl/ac). This corresponds to a contribution of 41% by bees, 30% by non-bee insects, and 29% by mechanical agitation. In the greenhouse, the agitation treatment had, on average, higher berry weight per upright (0.6 g/upright) than the undisturbed control treatment (0.04 g/upright), but again, not as high as the biotic treatment (3.0 g/upright). This confirmed that cranberry does not autogamously self-pollinate indicating that all yields are due to biotic or abiotic vectors moving pollen between flowers. Although bees clearly contribute to cranberry pollination, previous studies have understated the contribution of alternative mechanisms by which cranberry pollen can move between flowers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".