Cost analysis of a forest seedling planting machine: a case study for BC
Bibliographic record
Abstract
The Province of British Columbia forest tenure licensing practices require the replanting of tree seedlings in the place of logged forest. Reforestation is presently done manually by individual tree planters. Currently there is a shortage of skilled tree planting labour in Western Canada. This shortage will be exacerbated as the millions of hectares of dead lodgepole pine forests in British Columbia's Central Interior continue to be harvested in the wake of the mountain pine beetle epidemic. Mechanized planting provides a possible solution to this problem. This method has been applied successfully in the agriculture sector and attempted in forestry. Presently in Western Canada no commercially viable automated reforestation is taking place. Research into mechanized conifer transplanting techniques introduced thus far show low productivity and high operating expenses. This paper investigates current manual and automated planting methods for seedling planting quality, growth rates and transplanting costs. In order to contribute to design criteria and assess the commercial prospects of mechanized reforestation equipment in British Columbia's Central Interior, estimates of a machine's minimum planting rate, crew size and equipment configuration will be made. From these specification an initial capital investment in equipment is forecast and projections of operating costs determined. --P. ii.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".