Exploring the sustainability of current management prescriptions for Pinus caribaea plantations in Cuba: a modelling approach.
Bibliographic record
Abstract
BLANCO JA & GONZALEZ E. 2010. Exploring the sustainability of current management prescriptions for Pinus caribaea plantations in Cuba: a modelling approach. The ecosystem model FORECAST was used to evaluate the sustainability of current management practices in Pinus caribaea plantations in Pinar del Rio (western Cuba). Model predictions were within the range of observed field measurements of height, diameter, stem density and volume. The model performed reasonably well in capturing general growth trends (r values for dominant height, diameter and merchantable volume were 0.91, 0.77 and 0.81 respectively). In the second part of our work, model output of merchantable volume, stem biomass, soil organic matter and available N in soil were analysed in 18 different combinations of rotation length (25 vs. 50 years), thinning intensity (0, 15 and 30% stems) and fertilisation (0, 50 and 100 kg ha1 N) in order to study the effects of different management regimes on site fertility. Our results indicated that some of the current prescriptions could produce a considerable loss of nitrogen, and in some cases, a decrease in productivity after the third 25-year rotation. However, other prescriptions can keep productivity and soil organic matter at acceptable levels. The results of our analysis illustrated the portability and utility of FORECAST as a scenario-analysis and decision-support tool in managing pine plantations in the Caribbean region and, potentially, elsewhere.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".