Using harvester data from on-board computers: a review of key findings, opportunities and challenges
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
Abstract Single-grip harvesters are equipped with an on-board computer that can normally collect standardized data. In times of increased mechanization, digitalization and climate change, use of this extensive data could provide a solution for better managing calamities-outbreaks and gaining competitiveness. Because it remains unclear in which way harvester data can contribute to this and optimization of the forest supply chain, the focus of this review was to provide a synopsis of how harvester data can be used and present the main challenges and opportunities associated with their use. The systematic literature review was performed with Scopus and Web of Science in the period from 1993 to 2019. Harvester data in form of length and diameter measurements, time, position and fuel data were used in the fields of bucking, time study, inventory and forest operation management. Specifically, harvester data can be used for predicting stand, tree and stem parameters or improving and evaluating the bucking. Another field of application is to evaluate their performance and precision in comparison to other time study methods. Harvester data has a broad range of application, which offers great possibilities for research and practice. Despite these advantages, a lack of precision for certain data types (length and diameter), particularly for trees exhibiting complex architecture where the contact of the measuring wheel on the harvesting head to the wooden body cannot be maintained, and position data, due to signal deflection, should be kept in mind.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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