Capital Planning under Uncertainty at Fletcher Challenge Canada
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
Fletcher Challenge Canada Limited (FCCL) is a large pulp and paper producer in British Columbia. FCCL has traditionally been a newsprint producer and has kept pace with increasingly stringent quality requirements by continually rebuilding existing paper machines and ancillary plant. The company is also considering other options, including converting machines to different grades of paper. Because of the complexity associated with the large number of possible options available, the company decided to develop an optimisation model to assist with this strategic decision making. Market forecasts, capital requirements, production and other pertinent data were collated for a ten year planning horizon, and incorporated into a multi-period optimisation model.. Initially this model proved to be extremely difficult to solve. Based on knowledge of the business a number of extra constraints were added that improved its performance and allowed optimal solutions to be obtained. The model has proved to be successful in challenging entrenched views within FCCL regarding strategic direction, and stimulating wide ranging thought and discussion.
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 | 0.000 | 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) | 0.012 | 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