Seedling regeneration at northern treeline, Northwest Territories tundra
Bibliographic record
Abstract
Global warming has had an amplified effect in northern environments (i.e. Arctic and Subarctic regions). An indirect result of this warming is what is known as Arctic greening, which is the increase of photosynthetic material, or plant matter, in Arctic environments. How this greening trend is represented by trees at latitudinal (or northern) treeline is largely unknown. To determine how treeline may respond, I am investigating the physical environment surrounding seedlings found growing at treeline in the Northwest Territories, as well as the reproductive capacity of the mature trees in this region. Physical characteristics of sites which contain seedlings are compared to sites within the same region which do not in an attempt to determine what aspects of these environments are significant in the establishment of seedlings at treeline. Site characteristics include vegetation cover, distance to mature trees, and distance and dimensions of the nearest shrub. Reproductive capacity of mature trees is also tested to determine how significant seed viability may be in generating seedlings in this region. The limiting factor in treeline expansion may be an issue of pre-dispersal (i.e. viable seed production) as opposed to post-dispersal (i.e. seedling growth). I am conducting a germination test where I have extracted seeds from fifteen trees dispersed throughout the treeline region and have placed them under ideal growth conditions for an honest depiction of viable to unviable seed ratios. These tests may show conclusive evidence regarding what factors are contributing to treeline dynamics within a changing environment.
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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 | 0.001 | 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) | 0.002 | 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".