Bibliographic record
Abstract
Real option theory has for some decades been used in forest economics to analyse problems that involve irreversible and stochastic decisions. One of these problems is when to harvest a stand and with what species to regenerate. This problem is analysed here, assuming that the value development of both the present and the next stand is stochastic and that the regeneration species must be chosen. The latter decision is assumed asymmetric, as natural regeneration can be exchanged for a planted regeneration but not vice versa. Consequently, the decision can be postponed for some time if natural regeneration is foreseen, making a species change possible if favoured by the value development or if regeneration of the present species proves unsuccessful. This is an extension of the traditional two-option problem, by involving several stochastic elements that are nested in another option problem: when to harvest a stand. Thus the decision on when to harvest a stand depends on its present value as well as on the value of the future stand, both values evolving stochastically. The problem is formulated as a sequential two-option problem and solved numerically.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".