Evaluation of Adaptive Responses of Juglans neotropica Diels Progenies Based on Dasometric Traits
Bibliographic record
Abstract
(1) Background: Juglans neotropica Diels, native to the Andes, is highly valued for its quality wood, medicinal uses, edible fruits, and natural dyes. However, its population has been greatly reduced due to overexploitation, becoming threatened and resulting in genetic stochasticity. Therefore, a prompt ex situ and in situ conservation effort is needed for its conservation and restoration. (2) Methods: A total of 439 trees of J. neotropica Diels were sampled from selected individuals across the northern and central regions of Ecuador. These trees were planted in a randomized complete block design to assess their growth and genetic variability. (3) Results: Annual average tree growth varied significantly among progenies. Based on their dasometric traits, two distinct groups were identified as superior and surveillance. Five trees demonstrated promising traits (TJ182, ChL2314, ChL142, TJ1310, and BSM14), suggesting potential for inclusion in forest genetic improvement programs. (4) Conclusions: Fifteen percent of individuals from the five studied provenances exhibited desirable dasometric characteristics and high-quality wood. In addition, several individuals within the progenies exhibit resistance to biotic agents, indicating a genetic potential for disease.
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 | 0.001 | 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".