Boundary Detection in Altitudinal Treeline Ecotones in the Spanish Central Pyrenees
Bibliographic record
Abstract
Spatial identifcation and description of ecological boundaries is fundamental to better understanding of treeline dynamics. Ecological boundaries across two contrasting subalpine Pinus uncinata forest-alpine grassland ecotones were delineated within the Central Pyrenees (Ordesa and Tessó sites). Boundaries were delineated using an edge detection algorithm for two-dimensional data (lattice-wombling). Tree density, size-structure, growth-form, and estimated age were used to reveal spatial location of boundaries for several size and growth-form tree classes. Overlap statistics were applied to quantify spatial relationships among boundaries determined for different sets of variables. The most significant and consistent boundaries were those for structural variables at the Ordesa site. At this site, the sequential disposition of bigger and unistemmed trees descending across the ecotone produced boundaries for size-structure and growth-form variables. These boundaries were located along an ordered spatial pattern (altitudinal diagonal). At the Tessó site, there were few consistent boundaries, most of which were developed along the slope. Overlap statistics showed that boundaries at the Ordesa site were more spatially related than were those at the Tessó site. This result held when any set of variables was considered. The studied ecotones describe sharp (Ordesa site) and gradual (Tessó site) structural changes in tree populations, related to situations similar to the ecotone and ecocline concepts, respectively. The possible environmental driving factors producing these patterns are the strong winds and reduced snow cover at higher altitudes at the Ordesa site, and snow avalanches at the Tessó site. Boundary detection through time in permanent plots might be a better tool for monitoring climate-change impact in the forest-alpine grassland ecotone than the subjective location of treelines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".