Harvesting Systems and Equipment in British Columbia Ministry of Forests Forest Practices BranchHarvesting Systems and Equipment
Bibliographic record
Abstract
The handbook does not attempt to define a single “best ” system for any site. Instead, it presumes that readers need to be aware of the key factors that influence the probability of achieving success with any given combination of equipment and site characteristics. Readers will then use their own judgement to evaluate the merits of the various options. The information in the handbook should be considered only as part of an overall process for equipment selection which will vary from company to company. To improve their productivity, loader-forwarders are used to great advantage with other machines such as grapple yarders and skidders, especially when working around sensitive zones. The loader-forwarders can travel close to the sensitive zones, extract the logs without encroaching on the protected areas, and place the logs in a more advantageous position for the primary equipment to reach. This technique can be more economical for the overall system. Excavators are highly mobile machines that can traverse a wide variety of terrain. The grapple can be used to stabilize the machine on steep ground to minimize the danger of overturning. Since there is only one worker with the machine, the hazard to other personnel is low. Sideslope and soil type govern the limit to safe operation. The maximum sideslope for using a loader-forwarder is about 25–35%, especially if the sideslope is uniform. For more broken terrain, loader-forwarders can operate on steeper terrain, provided that they can travel and work safely on an acceptable route through the steep ground. Loader-forwarders cannot work safely on thin soils overlaying bedrock because of the danger of sliding. However, the hazard can be reduced by using flotation mats. The sliding hazard is aggravated with snowfall.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 teacher head, 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".