Fine‐root exploitation strategies differ in tropical old growth and logged‐over forests in Ghana
Bibliographic record
Abstract
Abstract Understanding the changes in root exploitation strategies during post‐logging recovery is important for predicting forest productivity and carbon dynamics in tropical forests. We sampled fine (diameter < 2 mm) roots using the soil core method to quantify fine‐root biomass and architectural and morphological traits to determine root exploitation strategies in an old growth forest and in a 54‐yr‐old logged‐over forest influenced by similar parent material and climate. Seven root traits were considered: four associated with resource exploitation potential or an ‘extensive’ strategy (fine‐root biomass, length, surface area, and volume), and three traits which reflect exploitation efficiency or an ‘intensive’ strategy (specific root area, specific root length, and root tissue density). We found that total fine‐root biomass, length, surface area, volume, and fine‐root tissue density were higher in the logged‐over forest, whereas the old growth forest had higher total specific root length and specific root surface area than the logged‐over forest. The results suggest different root exploitation strategies between the forests. Plants in the old growth forest invest root biomass more efficiently to maximize soil volume explored, whereas plants in the logged‐over forest increase the spatial distribution of roots resulting in the expansion of the rhizosphere.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".