Ten-year growth and survival of Douglas-fir seedlings treated with plant growth regulating substances at transplant
Bibliographic record
Abstract
Commercially available plant growth regulators (PGRs) or moisture retention gels, applied to the roots of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) before planting, can modify indole-3-acetic acid (IAA) levels in roots, root growth responses, and tree survival. We treated two different 1+0 stock types (PSB313B and PSB323) of Douglas-fir with indole-butyric acid (IBA), ethephon (Ethrel®), alginate, or a combination of IBA and alginate. New root growth and IAA levels in roots were measured 2 weeks after planting, and aboveground growth and tree survival were monitored over 10 growing seasons after planting. Treatment with IBA or the combination of IBA and alginate increased IAA conjugate and free IAA levels in roots, root growth, and tree survival. Alginate treatment alone increased new root growth and tree survival, but did not increase free IAA levels in roots. Ethrel® treatment increased free IAA levels and root growth, but had no effect on IAA conjugates or tree survival. A cost analysis suggests that use of certain PGRs or alginate decreases the cost required to attain target stocking and increased tree size. Our results suggest that application of PGRs or other root-promoting materials to the roots of Douglas-fir before planting has the potential to be a cost-beneficial method for increasing root growth and tree survival.
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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 | 0.001 | 0.001 |
| 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".