Чистая приведенная стоимость как индикатор экономической эффективности в лесном хозяйстве
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
The article explains the use of the net present value for the evaluation of the effectiveness of forest management strategies for specific sites. In Russian practice, this indicator is mainly used for the evaluation of investment projects, but in forestry developed countries such as Finland and Canada for several decades now this index is used to evaluate the effectiveness of management of forest areas and planning for logging and reforestation on them. This is due to the fact that in the forestry sector, as well as in investment projects a great role is played by the factor of time, i. e. flows of revenues and expenses can be considerably spaced apart in time. This means that the use of indicators such as net income, profit, profitability, etc. do not allow to obtain complete information and give distorted results, as the time factor is not taken into account. Using an integrated model of economic evaluation in the context of strategies may also lead to an increase in the volume of selective logging, because their benefits can be assessed more clearly.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.191 |
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