Influence of treatment on rooting of arctic <i>Salix</i> species cuttings for revegetation
Bibliographic record
Abstract
Increased northern exploration and resource extraction highlight a need for effective revegetation techniques to restore disturbed environments. This study assessed effects of indole-3-butyric acid (IBA), water extracts of Salix and smoke, soaking time, and collection time on adventitious and lateral root development of Salix species cuttings collected from Diavik Diamond Mine Inc., Northwest Territories. Over 80 percent of fall and spring cuttings developed adventitious roots, yet only 30 percent of summer cuttings rooted, indicating strong seasonal influences. Many cuttings developed extensive root system architecture in 60 days; some developed up to six orders of roots. Root length decreased with increasing root order in all seasons, and season influenced length within root orders. Application of IBA increased number of primary roots per cutting per season and number of cuttings with less than fifty secondary roots per primary root. Longer soaking times increased number of primary roots per cutting in different seasons, and soaking up to ten days increased longest root length. Salix and smoke water extract applications increased number of cuttings with twenty-five to seventy-four secondary roots. This research highlights the importance of treatment effects on adventitious and lateral root development to optimize root system architecture of cuttings from northern shrub species.
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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".