Bibliographic record
Abstract
The soil moisture has an important effect on growth and development of highbush blueberry (HB), mainly because the root system, devoid of root hairs, is superficial. Moreover, the texture and organic matter content of Korean soil is different from the main producing counties, such as USA and Canada. To facilitate the growth and development of HB and long-term maintenance of productivity, the research related to soil moisture condition in Korea should be the priority. This study was performed to investigate the growth properties of the HB in various soil moisture conditions in order to determine the irrigation trigger point and optimum soil water potential. The texture of soil used in this experiment was loam. For the experiments, the soil was mixed with peatmoss at a rates 30% (v/v). Irrigation was scheduled at -3, -4, -5, -8, -15 and -22 kPa soil water potential then investigated leaf macronutrient, bush growth, and fruit properties. The leaf K content of HB showed the same trend in the soil water potential, but Leaf P and Mg content was highest in -5 and -22 kPa, respectively. The productivity and growth amount of HB showed the peak at the range of -4~-8 kPa as normal distribution pattern, and greatly decreased at above -15 kPa. Total dry weight and Cane diameter were highest at -4 kPa, plant width, fruit weight and yield were highest at -5 kPa, and plant height, cane number and shoot tension were highest at -8 kPa. Soluble solids content showed same trend in the soil water potential, but titratable acidity, anthocyanins and total polyphenols were not significantly different. Therefore, the optimal soil water potential for the development and a maximum production of HB were a range of -4~-8 kPa, and the recommended ideal irrigation trigger point was within -15 kPa.
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".