Differential growth response to increasing growing stock and structural complexity in even- and uneven-sized mixed <i>Picea abies</i> stands in southern Finland
Bibliographic record
Abstract
Widespread application of the selection system will depend on whether creating stands with uneven-sized (UES) structures comes at the expense of net periodic annual basal area increment (PAI) compared with stands with even-sized (ES) structures. I modeled PAI on growing stock and structural complexity over 12 years in midrotation stands with ES and UES structure types. Average PAI of the largest trees did not differ among types and decreased with increasing stocking, whereas PAI of the smallest trees in the UES type declined with stocking. Trees ≥ 10 cm in diameter grew more slowly in the ES type than the UES type, but no difference was seen after incorporating small trees (down to 5 or 2 cm). In the ES type, PAI of most trees increased linearly with increasing stocking, whereas in the UES type, it increased only up to ∼20 m2·ha−1. As structural complexity increased, PAI of the largest trees increased in the ES type, whereas PAI of all but the largest trees decreased in the UES type. Neither silvicultural system was innately more productive, as each can outperform the other under optimal levels of stocking and structural complexity. However, optimizing increment of only large trees would undermine the UES type.
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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 | 0.001 |
| 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 | 0.001 | 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".