Assisted Revegetation in a Subarctic Environment: Effects of Fertilization on the Performance of Three Indigenous Plant Species
Bibliographic record
Abstract
Assisted revegetation is particularly difficult in subarctic and arctic ecosystems where the impact of anthropogenic activities can be extensive and natural plant regeneration is slow. The construction of a military base in the 1950s at Kuujjuarapik–Whapmagoostui in northern Quebec destroyed most of the vegetation cover. Afterwards, other anthropogenic disturbances linked to the village expansion (housing, ATV traffic, pedestrian trampling) have slowed down the recovery process. To provide residents with low-cost but efficient assisted revegetation techniques, we evaluated the performance (seedling emergence, survival, and biomass production) of three indigenous plant species (Leymus mollis, Lathyrus japonicus, Trisetum spicatum) submitted to different levels of mineral and organic fertilizer additions in both a greenhouse experiment and a field plantation in the village. In the greenhouse experiment, moderate mineral fertilization had positive impacts on seedling emergence and both aboveground and belowground biomass of L. mollis. The magnitude of this impact on biomass was greater when mineral fertilization was combined with organic fertilization. The effects of mineral fertilization were negative on the other two species, especially at higher fertilization levels. However, after two growing seasons, a moderate level of mineral fertilizer in the field plantation had positive effects on the cover and aboveground biomass of all three species. Overall, organic fertilization from the substrate of a nearby marsh did not enhance plant performance in either experiment. Planting seeds of L. mollis or T. spicatum in combination with a moderate level of mineral fertilization at the time of planting provides a low-cost assisted revegetation treatment for subarctic villages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".