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Bibliographic record
Abstract
A common problem in forestry is plant death caused by frost. The risk of frost is highest on wind protected, flat or low-lying areas in the terrain. Here you often have regeneration problems with Norwegian spruce which is especially sensitive to spring frost. Swedish tree experiments have shown that the black spruce can be a suitable alternative on these areas, for example on moisture frost prone areas. The species originates from North America and its pioneer tree characteristics make it more frost hardy than Norwegian spruce. It is also relatively free from damages and in Canada the light wood makes it sought after as pulpwood. \nIn this study areas suitable for culturing black spruce in northern Sweden were identified, where it can compete with Norwegian spruce. \nBy the creation of a map model based on different map material, the suitable areas could be selected. First, a slope model including low-lying and flat areas of a certain size was created. Thereafter a selection of the different land covers was made in a map layer. The land covers that were selected were forested mires, peatery and swamp forests. When the slope model and the selected land covers were run together it resulted in a polygon map with areas that met the requirements of land cover as well as slope and surface area. To be able to present the result in a suitable way, the area within Åsele municipality was chosen as delimitation. The total area of suitable black spruce areas within Åsele municipality was calculated to 7118 ha. That corresponded to about 1,6 % of the total land area, when water surfaces was excluded. \n
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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 source (direct Gemma or distilled Codex), 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".