Predicting green and dry mass of yellow-poplar: an integral approachWest Virginia Agriculture and Forestry Experiment Station Scientific Article 2962.
Bibliographic record
Abstract
An integral approach to estimating stem green and dry masses for yellow-poplar (Liriodendron tulipifera L.) in West Virginia was compared with traditional ratio equation methods. The data were based on stem analysis of 26 trees from the Central Appalachian Broadleaf Forest province in northern West Virginia and 18 trees from the Eastern Broadleaf Forest province in west-central West Virginia. Sample disks were extracted from 0.3 m, 0.6 m, 1.37 m, 1.8 m, and then every 1.2 m to an approximate 7.6 cm top diameter outside bark. Sample trees ranged from 17.3 cm to 56.1 cm in diameter and from 18.8 m to 38.5 m in total height. The proposed equation generally performed better for the whole tree as well as for sections within the tree based on the nine relative height classes examined. A constant wood-density equation was superior to the use of a linear wood-density equation as a function of height above the ground. The proposed equation explained over 90% of the variation in stem mass and compared favorably with existing fixed merchantable top mass equations.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".