Effects of Light Gaps and Litter Removal on the Seedling Performance of Six African Timber Species<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT Valuable timber tree species frequently show poor regeneration after selective logging in tropical forests. Small size of logging gaps, lack of soil disturbance, and limited seed availability have each been blamed for observed regeneration failures. We investigated seed germination and seedling performance using a split‐plot factorial design involving light availability and litter removal for six Central African timber tree species, hypothesizing that canopy gaps and litter removal would improve seedling establishment, and that less shade‐tolerant species would show stronger responses to both factors. Contrary to our expectations, significantly more germinants established on intact litter than on exposed mineral soil 3 mo after seeding. After 18 mo, seedling survival, height and diameter growth, leaf area, and rooting depth were all much higher in gap plots than in the understory for all species, with the exception of Gilbertiodendron dewevrei, a highly shade‐tolerant species whose survival was higher in the understory. Leaf production was negatively influenced by litter removal in the least shade‐tolerant species, Nauclea diderrichii, with weak or positive effects in other species. G. dewevrei, while displaying a low‐light threshold for growth, exhibited a surprisingly high growth response to increasing light comparable to more shade‐intolerant species, a response that may help explain its local competitive dominance in the region. Due to the rapid closure of small gaps, we suggest that shade‐intolerant species such as N. diderrichii, Khaya anthotheca, and Entandrophragma utile might benefit from more intensive silvicultural practices that create larger canopy gaps.
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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".