Genotypic variation and recovery of <i>Populus tremuloides</i> from biomass removal and compaction in northern Wisconsin, USA
Bibliographic record
Abstract
This study was initiated in 1990 to determine the effects of simulated logging practices on long-term productivity of trembling aspen (Populus tremuloides Michx.). Treatments included three levels of biomass removal (entire aboveground woody biomass, control; control + coarse woody detritus, LS; and LS + forest floor, FF) and two levels of compaction (light compaction on winter skid roads, TRA; heavy simulated compaction with FF, COM). The study was conducted on a Typic Haplorthod and a Haplic Glossudalf of medium site quality (site index50 years = 21 and 23 m, respectively) on the Brule State Forest. Twelve years after treatment, the following results were noted: (1) there were no significant differences in aspen height growth among treatments at either site except for lower stocking, height, diameter, and basal area on heavily compacted plots (COM, forest floor removed before compaction) at the clay site; (2) there was considerable genotypic variation in aspen height growth; and (3) recovery of physical properties, as reflected by bulk density, occurred within 12 years of treatment but was not manifested by improved aspen growth. These results suggest that concern over long-term effects of intensified biomass removal and soil compaction should be matched by a concern over protection of the aspen gene pool in the upper Great Lakes region.
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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) | 0.000 | 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".