Early Selection of Tree Species for Regeneration in Degraded Woodland of Southeastern Congo Basin
Bibliographic record
Abstract
Miombo woodland (MW) has several multi-purpose forest species, which are over-exploited for agriculture, charcoal and logging. Industrial plantations are among the promising solutions for sustainable management of MW, but high-yielding MW species are still lacking in the forestry sector. In this context, the present study assessed the growth of MW species, with respect to their early (ES) or late (LS) successional status. Seedling development was assessed for eight tree species, which were ES (Combretum collinum, Pterocarpus tinctorius) and LS (Brachystegia boehmii, B. longifolia, B. spiciformis, B. wangermeana, Julbernardia globiflora, J. paniculata), 1, 2 and 4 years after planting. Germination and survival varied in relation to ontogenetic traits of the species, but not the successional status, as was the case for growth and productivity. Seed germination was 60% to 88% for all species (except C. collinum). Seedling survivorship was greater than 70% for most species, except for J. paniculata (36%). ES species have greater growth compared to LS, due to the allocation of biomass to diameter and the length of the root, respectively, from the early stage of seedling development. These two strategies of biomass allocation are positively correlated with height. We further observed two distinct phases of seedling development (0–2 and 2–4 years) for all species. Growth differences among LS species were identified 4 years after planting. Based on seedling biomass, B. spiciformis, C. collinum and P. tinctorius are the most productive potential candidates for reforestation of degraded MW. We concluded that seedling growth and productivity varied according to strategies of biomass allocation that were related to successional status (ES (Chipya group) versus LS (Miombo group)) and to age following planting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".