Early influences of tamarack (<i>Larix laricina</i>) on black spruce (<i>Picea mariana</i>) and its immediate environment in plantations
Bibliographic record
Abstract
Mixed plantations are garnering increased attention due to their potential to provide a broader array of benefits compared to monocultures. Although numerous studies have indicated promising complementarity between black spruce ( Picea mariana) and tamarack ( Larix laricina), few have delved into individual tree growth interactions to thoroughly assess early growth complementarity. We sampled 119 planted black spruce and their immediate environment to quantify and qualify any differences between two conditions in young plantations: mixed tamarack (“mixtures”) and black spruce monocultures (“monocultures”) within young plantations. We investigated the effect of neighbouring under four perspectives: tree competition, microenvironment, foliar nutrients, and soil nutrients. Our results showed increased values for black spruce foliar nitrogen total concentration, soil pH, and canopy closure in mixtures compared to monocultures. Furthermore, black spruce stem volume was increased by 38.1% in mixture compared to monoculture. Black spruce stem volume was negatively affected (86% decrease) by the combined effect of shrubs and non-crop trees under high competition pressure, despite the plantation being mechanically released in 2017. Collectively, our results suggest that black spruce growing in mixtures holds a greater growth potential than black spruce in monocultures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".