Root development on cuttings of seven arctic shrub species for revegetation
Bibliographic record
Abstract
Vegetation removal during resource extraction in the Arctic causes long-lasting impacts requiring revegetation to accelerate plant reestablishment. This study focused on root development on shrub cuttings from seven common species at Diavik Diamond Mine, Northwest Territories. Two experiments were conducted; the first had six soaking times (zero, one, three, five, ten, twenty days), four indole-3-butyric acid (IBA) concentrations (0, 0.1, 0.4, 0.8 percent), and three seasons (summer, fall, spring). The second had a control, three IBA concentrations (0.1, 0.4, 0.8 percent) or alternative chemical compounds, either three Salix water or three smoke water extracts, in two seasons (summer, fall). After sixty days, all species developed at least primary and secondary roots in at least one season in one experiment, including one previously undocumented species, Kalmia procumbens. Rooting characteristics were highly variable, with maximum percentage of rooted cuttings from 3 to 55 percent and maximum number of roots per cutting from 1 to 117 across species, seasons, and experiments. Though rooting percentages were low, species-specific interactions between season and Salix water extract and smoke water extract were observed. Assessing multiple species highlights the potential of vegetative propagation to revegetate northern disturbed sites with common species that lack reliable seed sources.
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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) | 0.000 | 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 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".