A South African softwood sawtimber supply chain case study
Bibliographic record
Abstract
Supply chain management principles were analysed by investigating the effects of smaller-scale and incremental interventions in a forest-to-mill value chain on financial returns and forest resource use in an Eastern Cape case study area. Three previous studies provided input by determining fibre balances, a terrain factor, and primary and secondary transport travel speeds and efficiencies. Network analysis, combined with raster-based GIS, analysed different primary and secondary transport scenarios. The forest road network was repeatedly refined through theoretical removal of lower-class roads and subsequent upgrades of remaining roads, and the timber resource flowed over the remaining road network to the mill. Four road networks, including the existing and unrefined network, were studied. With sequentially improved secondary transport travel speeds, primary transport efficiency and fibre use, the net financial returns of the various scenarios were determined by applying discounted cash flow analysis (NPV). To address all possible combinations, 144 unique scenarios were created. The highest NPV achieved was R300.8 million associated with a highly upgraded road network and associated fast secondary transport speeds, cable skidder extraction, motor-manual felling and cross-cutting at the merchandising yard, all factors at optimal performance. The lowest NPV was R40.4 million associated with a simplified road network, low secondary transport speeds, cable skidder extraction, mechanised felling, and roadside merchandising and at status quo systems performance. Examination of individual factors found systems performance, secondary transport speeds and road network had the greatest influence, with systems performance and fibre losses providing the largest impact. Secondary transport speed followed as nine of the top 10 NPV scenarios were achieved with the highest possible road design speeds. Higher-class networks consistently outperformed the baseline and simplified scenarios. Harvesting system had limited effect. When operating at peak performance, using a merchandising yard becomes a better choice. There was no clear difference in terms of felling method or skidder type. It is clear that the optimised use of potentially the most productive machine, for example in one system, does not provide the best final results and that it is the basic harmonisation of all factors that must be taken into account. As in all three previous and related studies, the human element played a role.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".