Evaluation of the economic impacts of length and diameter measurement error on mechanical harvesters and processors operating in pine stands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Value recovery studies from around the world have shown that on average mechanical log-making systems lose 18% of the potential value compared to 11% for motor manual systems. One of the potential reasons for their poor value recovery performance is the level of accuracy of their stem diameter and length measurements. Numerous studies have looked at the level of error in both the diameter and length measurements made by mechanical harvesters and processors; however, few have looked at the economic impacts of these errors. The paper investigates the economic impacts in terms of value loss of six different harvesting operations in three different pine species. The accuracy and precision of the measurements recorded in this study were similar to those of other studies from around the world. A simulation model was developed to estimate the value loss caused by these errors. The results of the simulation model showed that the operations were losing between 3% and 23% of the potential value because of measurement errors. Further analysis showed that the industry should concentrate on increasing the precision of the length and diameter measurements to optimize gains from reducing the measurement error rates.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it