A new method for capturing stem taper variation for trees of diverse morphological types
Bibliographic record
Abstract
Understanding variation in tree stem form is fundamental to both ecological and economic assessments of forest ecosystem structure and function. Stem taper models (STMs) are widely used to describe tree form, but it can be challenging to apply them to trees with stems that diverge from an idealized norm, often leading to the exclusion of many trees from stem taper studies. Here, new “whole-tree” form type classes are advanced as simple and useful groupings for capturing stem form variation of trees of diverse morphological types and tested with a large tree data set without exclusion criteria. New form type classes explained much more of main stem form variation than knowledge of tree species, while “merchantable” form types explained the most variation between trees and stands. Broad-leaved species were much more likely to have complex stem forms than needle-leaved species, but species “evergreenness” was a very weak predictor of stem form variation when tree- and stand-level form variation was accounted for. A new, generalized framework for stem taper modeling is demonstrated, using both species and merchantable form types to capture tree-level random effects. New form types and the STM approach are relatively easy to apply and should be relatively simple to integrate into any conventional forest inventory system. Overall, the study demonstrates the importance of including and accounting for the diversity of observed stem forms in developing STMs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".