(321) Rapid and Differential Rates of Root Browning in Apple Trees under Different Irrigation Treatments
Bibliographic record
Abstract
As roots change color from white to brown, their absorptivity for water and nutrients typically diminishes. The effects of irrigation on root pigmentation were studied during 2003 and 2004 in Summerland, British Columbia, using an experimental orchard of `Golden Delicious' apple trees on M9 rootstocks. Root pigmentation was monitored weekly over the growing season using a minirhizotron camera inserted into clear plastic tubes in the root zone. Each tree had two emitters, one on either side of the bole and ≈30 cm from the trunk. Four irrigation treatments were tested: full irrigation with replenishing 100% of daily evapotranspiration (ET) on both sides of the tree (100% both), 50% ET irrigation on both sides (50% both), irrigating alternating sides of the tree with 50% ET (50% alternating) and one-sided irrigation at 50% ET (50% one-side). The 50% alternating irrigation treatment simulated the irrigation practice of partial root zone drying where irrigation was alternated about weekly from one side of the tree to the other. Root pigmentation was remarkably fast among these trees, with median days to browning ranging from 4 to 10 days among treatments. For 50% one-side trees, root pigmentation on the dry side of the tree was much faster than roots on the wet side (4 and 7 days, respectively; P < 0.007). Otherwise, no additional significant effects of irrigation on pigmentation were detected.
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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 | 0.001 |
| 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".