Influence of honey bee ( <i>Apis mellifera</i> ) on kiwifruit pollination and fruit quality under Australian conditions
Bibliographic record
Abstract
Abstract The influence of honey bee ( Apis mellifera L.) pollination of kiwifruit ( Actinidia deliciosa (A.Chev.) C.F. Liang et A.R. Ferguson) under different pollination regimes (viz. honey bee supplementation, honey bee saturation in a cage with a single male cultivar, and honey bee exclusion) was investigated under Australian conditions during 1993/94 and 1995/96 seasons. Vines that had no access to honey bees had significantly ( P≤ 0.01) lower fruit set (24%) compared to honey bee supplementation (91%) and bee saturation (89%). The mean yield (kg/vine) and the mean number of fruit/ vine in bee‐supplemented and bee‐saturated treatments did not differ significantly, although vines that were excluded from honey bees produced significantly ( P≤ 0.01) lower yields. However, individual fruit weight in the bee‐saturated treatments was affected. There were significantly more small fruit in bee‐saturated vines than in vines that were supplementary pollinated by honey bees. Bee activity as assessed by the number of bee visits on flowers (bees/vine per min) was significantly higher on male vines than female vines during the first 2 weeks of the flowering period. Honey bees were the main contributor to pollination and fruit set, although low numbers of other potential insect pollinators such as ladybird beetles and hover flies were also observed. The mean seed numbers in comparable fruit from higher weight groupings (i.e., 70–89, 90–109, and ≥110 g) in bee‐supplemented and bee‐saturated vines did not differ significantly, suggesting adequate pollination and fertilisation of ovules in these two treatments. Vines that were caged with a single male cultivar produced fruit with significantly higher ( P≤ 0.01) total soluble solids concentration than did those that were honey bee supplemented. Possible reasons for the reduced mean fruit weight under honey bee saturation are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".