Deep Planting Has No Short- or Long-Term Effect on the Survival and Growth of White Spruce, Black Spruce, and Jack Pine
Bibliographic record
Abstract
Abstract Although studies in the past have reported that the deeper planting of conifers has no effect on seedling performance, most planting guidelines in use today still recommend that seedlings be planted to the rootcollar. Past studies were mostly observational, used bareroot seedlings, and often reported early results from just one or two depths of planting treatments. Most of the results available regarding planting depth for boreal species are anecdotal, although they are planted by the hundreds of millions every year. The present study reports no short-term (1 year) or long-term (15 to 19 years) negative effect of planting depth on the survival and height and diameter growth of black spruce, white spruce, and jack pine seedlings over three large, replicated experiments in the boreal forest of eastern and northern Quebec (eastern Canada). Four different depth treatments were compared, from manual planting at the rootcollar to the deepest mechanical planting treatment at 10 cm or more, making this the largest, longest-lasting study of its kind. Although, as expected, important differences in growth were present between species, all three commonly planted conifers reacted similarly to the planting depth treatments (no effect). This result can in part be attributed to an almost perfect control of frost heaving in the deepest two treatments. Planting depth effects were assessed using analysis of variance, multiple Tukey honestly significant difference, and uncorrected pairwise one-tailed t-tests to increase the probability of detecting a negative effect. Absolute differences and effect sizes (generally small and often positive with greater depths) were also analyzed.
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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 | 0.001 |
| 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".