An evaluation of estimating and indexing methods to simplify the determination of management treatment effects on raspberry yields
Bibliographic record
Abstract
The effectiveness of using several proposals to estimate or index yield and size of raspberries as an alternative to picking berries as they ripen was examined in two field plot trials over two seasons at two locations in south coast British Columbia. The evaluation included examination of general correlations of the proposed estimate and index values with fresh picked yield, comparison of the significant nutrient and inter-row management treatment effects on proposed method values with effects on fresh picked yield values, influence of individual cane variability to distinguish significant treatment effects, and the effect of N on plant components used to derive the estimate and index method. Correlation coefficients for all yield estimate and index method values with fresh picked yields were generally good. Crop management treatment effects determined by the estimate and index values, however, were not the same as determined by harvesting the berries as they ripened. This showed that the estimate and index method values were biased relative to picked yield. Cane-to-cane variability within individual treatment plots was sufficiently large that differences between treatments had to be greater than 10 to 15% to be significant at P < 0.05 when five canes were randomly sampled for index component measurements to represent the plants in the plot. The five canes sampled for each plot were 5 to 10% of all the floricanes in the plots of this study. The concentration and biomass N measurements that were possible on the floricane components that were sampled for the index methods showed that management treatments of the two trials of the study could have influenced berry development, and hence contributed to the bias of the estimate and index method values relative to fresh picked yield. Although the estimate and index methods were generally quite well correlated with fresh picked yield, caution is advised when they are used directly as alternatives to fresh picking to evaluate crop management treatment effects on berry yield. Further knowledge about the physiological changes that occur during berry ripening may provide opportunities to improve the estimate and index measurements. Key words: Raspberry, Rubus idaeus L., yield estimate, yield index, nutrient effects, nitrogen effects
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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".