Effects of Substrate Water Content on Morphology and Physiology of Rosemary, Canadian Columbine, and Cheddar Pink
Bibliographic record
Abstract
Two experiments were conducted to determine how different substrate volumetric water contents (θ equals volume of water per volume of substrate) affected morphology and physiology of three popular perennials using a capacitance sensor-automated irrigation system. In the first study, rosemary ( Rosmarinus officinalis ) was grown at one of eight θ set points ranging from 0.05 to 0.40 L·L −1 . In the second study, Canadian columbine ( Aquilegia canadensis ‘Pink Lanterns’) and cheddar pink ( Dianthus gratianopolitanus ‘Bath’s Pink’) were grown at one of nine θ set points ranging from 0.05 to 0.45 L·L −1 . Total leaf number and area as well as shoot fresh and dry weight of rosemary plants grown at θ of 0.20 L·L −1 or greater were approximately twice that of those grown at lower θ. Canadian columbine height increased as θ increased. Leaf area of cheddar pink grown at θ of 0.35 L·L −1 or higher was twice that of plants grown at the lowest θ. Shoot dry weight of Canadian columbine was not significantly affected by θ. Shoot dry weight of cheddar pink responded quadratically to increasing θ and peaked at θ of 0.35 L·L −1 . θ also significantly influenced photosynthetic activities; net photosynthetic rate (A N ) and stomatal conductance ( g s ) of Canadian columbine increased with increasing θ. A N of cheddar pink also increased as θ increased. Greater water volumes were applied to maintain higher θ set points. Irrigation water use efficiency (IWUE = shoot dry weight ÷ total amount of water applied per plant) of Canadian columbine and cheddar pink was not influenced by θ. Growth of all three plants was reduced when grown at lower θ; in the case of cheddar pink and Canadian columbine, this was attributable at least in part to reduced A N .
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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 | 0.001 | 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".