Efficiency of early selection in <i>Calycophyllum spruceanum</i> and <i>Guazuma crinita</i>, two fast-growing timber species of the Peruvian Amazon
Bibliographic record
Abstract
Bolaina (Guazuma crinita Mart., Malvaceae) and capirona (Calycophyllum spruceanum (Benth.) Hook. f. ex K. Schum., Rubiaceae) are fast-growing Amazonian timber trees. In Peru, they are increasingly being used in agroforestry systems and plantations, and interest in developing improved germplasm is growing. However, tree improvement incurs both direct costs and interest costs on investments; because of this, early selection is of interest. We examine the efficiency of early selection 13 or 17 months after field trial establishment. These are compared with selection after 49 or 53 months using two efficiency metrics: one based on discounted response to selection per unit of present value of cost, and the second on net discounted revenues, using discount rates of 5%, 10%, and 15%. Our metrics differed from those used in previous studies by taking into account direct costs, as well as costs of capital. We found that in most scenarios, early selection was attractive, partly due to direct cost savings. We conclude that in evaluating the efficiency of early selection, lack of consideration of direct costs may produce erroneous results. We also explore some general implications of the results.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 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".