Why this work is in the frame
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Bibliographic record
Abstract
We have reviewed Swedish forestry and hunting literature in order to investigate how the management of moose (Alces alces) in Sweden has changed during the 20th century, especially after the re-establishment of the wolf (Canis lupus) in the 1980s. The focus is on the perspective of moose hunters and of the forest industry since these are the two main factors in control of the size of the Swedish moose population. At about the same time as the Swedish moose population was reaching its all time high, there were reports that wolves were being spotted again in the country. Up to the first half of the 19th century, wolves were relatively abundant in Sweden. However, intense hunting led to their drastic decrease, so that in the beginning of the last century only a small number remained. As a result of being virtually extinct, the wolf was thus declared protected in 1965. Currently, the Scandinavian (i.e., the Swedish and Norwegian) wolf population has grown to a size of about 100 individuals. This might not sound like much in a relatively large country like Sweden but in areas where hunters already have had their culling ratio for moose decreased by the forest companies to minimize forest damage, the establishment of a single wolf pack has proven to be 'the final straw.' Thus, there are instances where hunters have gone on 'strike'; i.e., refusing to search for animals injured in traffic, as protest to this state of affairs. There are few instances (to our knowledge) where the forest companies have shown any increased interest in the 'wolf issue', which might be understandable from a commercial point of view but disastrous when it comes to their relationship with the hunters. We suggest that moose management in areas with wolves should be controlled by special regulations, taking both local and national interests into account and where ownership of the hunting ground should not be the sole consideration.
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) | 0.000 | 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