Effects of temporal heterogeneity of watering on size of an annual forb, <i>Perilla frutescens</i> (Lamiaceae), depend on soil nutrient levels
Bibliographic record
Abstract
Temporal heterogeneity of watering affects plant growth. When the same total amount of water is supplied, frequent watering leads to greater plant size than infrequent watering. However, the effects of a given watering regime can differ when nutrient levels vary. An experiment was designed to test the hypothesis that the effects of temporal heterogeneity of watering on plant growth also vary as a function of nutrient levels. Perilla frutescens (L.) Britton was grown using different combinations of nutrient levels and watering frequencies, with total water held constant across the treatments. The effects on plant size were analysed after 36 d. Under nutrient-rich conditions, frequent watering resulted in significantly larger plants than infrequent watering. However, under nutrient-poor conditions, no significant difference was detected between the different watering frequencies. The temporal heterogeneity of watering thus appears to have different effects on plant growth at different nutrient levels. Therefore, the watering heterogeneity should be examined with nutrients as unity, because the watering heterogeneity and nutrients affect plant growth in an interactive manner.
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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".