Spatial and temporal pattern of white spruce regeneration within mixed‐grass prairie in the Spruce Woods Provincial Park of Manitoba
Bibliographic record
Abstract
Aim Thirty white spruce [Picea glauca (Moench) Voss] islands were sampled to study the temporal and spatial pattern of white spruce regeneration at its southern limit of distribution. Location The study was conducted within three mixed‐grass prairies in the Spruce Woods Provincial Park (SWPP) of south‐western Manitoba. Methods White spruce seedlings, saplings and trees were mapped and measured in relation to eight sectors and five zones delimited by four transect lines extending through the centre of each island oriented north to south, west to east, north‐west to south‐east and north‐east to south‐west. Results Temporal patterns of regeneration were negatively correlated with July temperature at the time of establishment and up to 30 years after establishment. Height growth of seedlings and saplings were also negatively correlated with July temperature. Seedlings, saplings and trees were concentrated on the north vs. south aspect and within 4–12 m from the island centre. Seedlings grew almost exclusively in association with creeping juniper (Juniperus horizontalis Moench). Main conclusions Results of the study suggest that white spruce recruitment and growth reflect past climatic variation, and microclimatic and microsite conditions promoting soil moisture retention and/or fire risk reduction facilitate white spruce germination and establishment.
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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 | 0.001 | 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) | 0.001 | 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".