Special session — Value chain in natural resource sector: How to deal with uncertainty and multipurpose uses?
Bibliographic record
Abstract
Typical natural resource value chains such as forestry, agriculture, fisheries, and mining range from raw material sources through primary and secondary processing facilities, distributions channels to the markets (end customers). Uncertainty is a key issue across the value chain since it is inherent to nature itself and to the decision making context where uncertainty can rise due to social, economic, environmental or technological reasons. On the other hand, many stakeholders are involved in these value chains such as governments, industries, and commumties with conflicting objectives and multipurpose uses for resources. Managers must deal with increasing environmental regulations and concerns and social impact of their decisions processes. Optimizing value chain within such context becomes a challenging and complex task that requires new approaches and techniques. We invite researchers and practitioners to submit their recent work that address planning issues under uncertainty and multipurpose uses of natural resources. Researches dealing with stochastic programming, robust optimization, and multiobjective optimization in natural resource area including cases studies are welcome. Below are the main topics (in the conference's web site) that are related to the session we have proposed: 1 — Supply chain design and performance evaluation; 2 — Logistics, transportation, and distribution systems; 3 — Decision analysis and decision support systems. Here are some pertinent keywords for the session: • Value chain optimization; • Stochastic programming; • Robust optimization; • Uncertainty; • Natural resources (forestry, mining, agriculture, and fisheries); • Multipurpose. Finally, regarding the area of development: applied as well as fundamental contributions are welcome. Case studies within industry, governments and communities are very welcome to this session too.
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.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".