Spatial aspects of structural complexity in Sitka spruce – western hemlock forests, including evaluation of a new canopy gap delineation method
Bibliographic record
Abstract
Structural complexity in long-lived forests where stand-replacing disturbances are rare is thought to emerge from chronic small-scale disturbances and competitive interactions between trees. We analyzed tree size distributions, tree spatial patterns, and canopy gap attributes in ten 1.42 ha stem mapped plots in old-growth Sitka spruce (Picea sitchensis (Bong.) Carrière) – western hemlock (Tsuga heterophylla (Raf.) Sarg.) forests in southeast Alaska. Most plots had rotated sigmoid or reverse-J-shaped diameter distributions. Overstory tree patterns were uniform at short distance (<5 m) and random or aggregated at larger distances (>5 m); understory trees were spatially random or aggregated at most scales. Tree patterns were highly variable across plots. Overstory and understory tree populations were spatially independent in most medium canopy cover (40%–70%) plots but spatially repelled in most high canopy cover (>70%) plots. Canopy gap delineation using a traditional geometric approach identified more gaps and greater forest area in gaps compared to a new method based on canopy tree shadow lengths. We recommend defining the lower limit of canopy gap size using overstory tree crown diameter; gap delineation based on overstory tree shadow length is overly conservative at higher latitude sites. Our analyses show that, despite their low species richness, the temperate rainforests of southeast Alaska are highly structurally diverse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".