Variation and Correlations Among Stem Growth and Wood Traits of Calycophyllum spruceanum Benth. from the Peruvian Amazon
Bibliographic record
Abstract
Abstract Calycophyllum spruceanum Benth. is an important tree for timber and energy in the western Amazon, with expanding national and international markets for its wood. There is relatively little information, however, about geographic variation in tree growth and wood properties, and correlations among these traits. The first provenance trial was established with farming communities in the Peruvian Amazon. Seven provenances, sampled from regions near the equator, were tested in three planting zones located in one watershed. Variation and correlations were investigated in stem growth at 30 and 42 months, wood density (in the lower and upper parts of the stem) and mean heat content of stem wood at 32 months. Stem height varied significantly among provenances and planting zones, but zones accounted for much more variation than provenances. Stem wood traits did not vary significantly among provenances. Wood density was greater in the lower than in the upper stem. Wood density in the upper stem and the difference in density between the lower and upper stem varied significantly among planting zones: density in the upper stem was lowest, and the difference in density between the lower and upper stem was largest in the zone where trees grew most rapidly. Phenotypic correlations between stem growth and wood density differed in sign among planting zones, suggesting that selecting fastgrowing trees could indirectly reduce wood density in environments where trees grow slowly, and increase the difference in wood density between the lower and upper stem in environments where trees grow very rapidly. Correlations between stem growth and wood heat content were stable across zones, and indicated that larger trees tended to have wood with higher heat content. Stem-wood heat content varied with provenance latitude/ longitude in the sample region, but none of the other traits varied clinally. Results indicate that there is potential to select faster-growing provenances at an early age, but this could affect wood density in certain environments.
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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 | 0.001 |
| 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 | 0.000 |
| 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".