A South African softwood sawtimber supply chain case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it