Integrated resource management plan combining STELLA and Goal Programming models
Bibliographic record
Abstract
The primary objective of this study was to develop mathematical \nmodels with existing computer programs and to use these models \nto assist in providing useful information for multiple use planning. \nTwo different models were used: 1) a system dynamic model \n(STELLA) and 2) Goal programming (GP). These two types of the \nmodels were combined to complement each other. Timber, wildlife \n(represented by moose), and forest aesthetics were selected as the \nthree variables in this study. The modelling approaches of the two \nmodels were discussed. The STELLA model was developed based on \npast experience and knowledge, while the GP model was formulated \nbased on the simulation results of the STELLA model. Gross merchantable \ntimber and the dry weight of browse were used as goals \nin the GP model. Area constraints in the sensitive area (SA) zone \nwere used to indicate aesthetic potential. The use of the two models \nwas illustrated with a case study area, which is located in management \nunit 030 of Abitibi-Spruce River Forest, Northwestern Ontario. \nA thirty-year planning horizon and three management alternatives \nwere employed. The results show that the STELLA model can help \nthe forest manager to better his understandings of forest dynamic \nbehaviour, and the solution of the GP model was improved by the \nreasonable goal levels set by using the simulation results from the \nSTELLA model. As a result, the combination of two models made \nthe integrated resource management planning more suitable and \npractical.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".