Svartgran – ett alternativ när allt ser mörkt ut? : en kartmodell för att visa lämpliga ståndorter för odling av svartgran
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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