Nutrient loading <i>Artemisia cana</i> seedlings in greenhouse increases nitrogen tissue content and post‐outplanting survival
Bibliographic record
Abstract
Sagebrush ( Artemisia ) is a vital habitat component for many grassland species and continued loss, fragmentation, and degradation of sagebrush habitats has increased the need for their restoration. Conventional revegetation methods have resulted in extremely low plant establishment and survival inhibiting the success of sagebrush restoration efforts. During greenhouse production, we sought to improve Artemisia cana seedling quality and post‐planting growth and survival by increasing tissue nutrient content. We conducted a preliminary 12‐week greenhouse study and a subsequent 26‐week greenhouse + 2‐season field study, investigating 12 nutrient loading treatments (application of 70, 105, 175, and 245 mg nitrogen [N] plant −1 at single, constant, exponential, and modified exponential rates). Nitrogen content of seedling leaves and roots at 26 weeks indicated that application of 175 and 245 mg N plant −1 on exponential or modified exponential dosing schedules resulted in nutrient‐loaded seedlings. Seedlings were planted into a cleared plot in Grasslands National Park, SK. After 60 weeks in the field, nutrient‐loaded seedlings had significantly greater survival (75%) than non‐loaded seedlings (65%) and greater crown area (1,351 vs. 1,004 cm 2 ). Rhizomatous reproduction (sprouting) occurred within 4 weeks of planting. New sprouts had disproportionately larger crown areas (2,237 cm 2 ) and greater survival (86%) than planted seedlings. Nutrient loading was an effective method to overcome high mortality that has inhibited restoration outcomes. Where plant materials and resources are limited, nutrient loading could be especially useful by improving seedling survival and crown area without increasing planting densities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".