Effects of selective tree harvests on aboveground biomass and net primary productivity of a second-growth northern hardwood forest
Bibliographic record
Abstract
Restoring structural features of old-growth forests, such as increased canopy gap sizes and coarse woody debris, is a common management goal for second-growth, even-aged stands. We experimentally manipulated forest structure by creating variable-size canopy gaps in a second-growth northern hardwood forest in north-central Wisconsin following two growing seasons of pre-treatment monitoring. The objectives of this study were to quantify the influence of canopy gaps of different sizes (50–380 m2) on aboveground biomass and productivity of each vegetation stratum two growing seasons following treatment. Two years after treatment, ground layer biomass in canopy openings increased significantly relative to surrounding undisturbed transition zones. The response of ground layer biomass was greatest in the large versus the medium and small gaps. Sapling aboveground net primary productivity was significantly greater in undisturbed transition zones than within gaps across gap sizes following the second post-treatment growing season. Annual stem diameter increment was greatest for trees along gap borders and was correlated with crown class, percentage of crown perimeter exposed, gap area, and shade tolerance. Total aboveground net primary productivity was significantly lower in the gap addition plots the first year but by the second post-treatment growing season no longer differed from that in the control plots.
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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 | 0.000 | 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".