Comparison of decay classification, knife test, and two penetrometers for estimating wood density of coarse woody debris
Bibliographic record
Abstract
Inventories of the necromass of coarse woody debris typically involve measurements of density (e.g., kilograms per cubic metre) on a sample of logs, with densities of other logs estimated based on assignment to decay classes. Here, we compare two new devices for assessing density of woody debris, a spring penetrometer and a dynamic penetrometer, with the traditional decay classification and knife test in terms of the strength of the relationship with measured density and the consistency in measurements by four different people. Our evaluation was conducted in a diverse tropical forest and involved only a brief training period in each method. Classifications or scores from all four methods were only weakly correlated with measured density, and consistency among technicians in the measurement–density relationship was highest for the dynamic penetrometer. Therefore, we conclude that when training time is limited and the sampled logs can reasonably be assumed to be representative of all of the logs (e.g., an inventory of one site at one time), it is best to simply assume that the average density of the sampled logs is representative of nonsampled logs. For inventories involving multiple people, limited training, and cases where the sample average is likely to be unrepresentative, we recommend the dynamic penetrometer.
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".