A vegetative index of stand productivity based on tree inventory for predicting oak site index in the Central Hardwood Region
Bibliographic record
Abstract
Models for prediction of site index (SI) typically include only abiotic causal variables (e.g., soil) and lack biotic response variables (e.g., vegetation), which could exhibit greater sensitivity to important environmental factors affecting tree height growth. Our study objective was to evaluate Whittaker’s moisture condition index (MCI) (R.H. Whittaker. 1956. Ecol. Monogr. 26: 1–80) as a potential biotic variable for inclusion with conventional abiotic variables in oak (Quercus L.) SI prediction models. The MCI is the sum of relative abundances of inventoried plot tree species weighted by their moisture affinity classification. We compared regression parameters of conventional base models including only abiotic variables with exploratory models configured with abiotic variables and MCI for explaining variation of SI. The best abiotic model included only aspect. When MCI was included in the abiotic model, aspect became insignificant, resulting in a single-variable biotic model that accounted for increased SI variation. The MCI biotic model remained significant when tested with independent data from a distant location. The MCI is easily calculated using plot inventory data, and with further evaluation, it may be confirmed as a useful biotic variable in combination with abiotic soil and topographic variables for prediction of oak SI.
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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".