Modern Applied Science, Vol. 3, No. 3, March 2009, all in one file, Part B
Bibliographic record
Abstract
Forest road construction for harvest operation are always been subjected to certain constrictions and limitations.Engineering practices on forest road alignment are hindered by costly environmental and operational assessment.GIS tools and related data such as remote sensing allows in allocating suitable access road by taking consideration of environmental and cost implication.The aim of this study is to present the method of integration of remote sensing data and GIS in allocating access road for forest harvesting using best path modeling.Therefore, the specific objectives of this study are to allocate the optimal forest roads network in forest operation, and to determine the density of forest road network.Allocating the best paths for forest road access for timber harvesting is a problem that can be solved by computer based approaches using spatial modeling.Spatial modeling is used to compute the indicative factors that suit road allocation.The model developed and designed using GIS to propose feasibility forest road allocation in the hill area.The method was designed to produce road layouts taking topographical features and forest environmental constraints into special consideration.In this study, four grid themes influencing the road construction were identified; elevation, slope, barrier of lake and distance to existing roads.The total of access road aligned and proposed in the respective area was 28,745.35m.Meanwhile the overall density calculated in selected compartments was about 9.93m/ha (0.80%).The densities of road paths presented here were achieved below as outlined by the forestry department.Thus, there is potential to reduce damage to the residual stand and to the ground area disturbance by the harvesting operation.The forest road alignment and information in this study provides an initial foundation on which GIS can be used for this kind of analysis in forest road planning.The result is not only associated with forest transportation, but at the same time is useful to identify a risk of road construction to the environment.This revealed that the minimum density of forest road construction can help mitigate the loss of ecological services of tropical forest subject to logging pressure and lead to greater financial benefit in future operations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.427 | 0.178 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".