Seasonal growth dynamics and carbon allocation of the wild blueberry plant (<i>Vaccinium angustifolium</i>Ait.)
Bibliographic record
Abstract
Kaur, J., Percival, D., Hainstock, L. J. and Privé, J.-P. 2012. Seasonal growth dynamics and carbon allocation of the wild blueberry plant ( Vaccinium angustifolium Ait.). Can. J. Plant Sci. 92: 1145–1154. Field studies were conducted at the Wild Blueberry Research Station, Debert, NS, to examine the carbon allocation dynamics within the wild blueberry (Vaccinium angustifolium Ait.). This was achieved with biweekly measurements of dry weight, soluble sugar and starch levels of the rhizomes, roots, stems/leaves and berries of plants in the vegetative (i.e., sprout phase) and cropping phases of production. Non-structural carbohydrate levels were determined using high-performance liquid chromatography (HPLC). Growth parameters included phenology, stem height, dry weights of the above-ground vegetation (stems and leaves), berries, rhizomes and roots. Interestingly, root growth was observed prior to upright shoot emergence and dry weight for rhizome remained higher compared with stems and leaves. The rhizomes acted as a carbohydrate source during stem and root growth. The developing berry crop appeared to be a strong sink for photo-assimilates, as berries were found to import sucrose and convert it to fructose and glucose during maturation, and HPLC studies further confirmed the increasing levels of fructose and glucose. Given the phenology of the wild blueberry, the results exemplify the importance of the rhizomes as a strong carbohydrate source, especially in the early stages of a growing season when the carbohydrate production is limited.
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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) | 0.001 | 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".