Range characteristics and productivity determinants for reindeer husbandry in Sweden
Bibliographic record
Abstract
Factors expected to affect reindeer productivity, and their spatial and temporal variation within the reindeer husbandry area in Sweden, were examined through multivariate statistical analyses. Data for the studies were extracted from different mapped databases and statistics from the herding districts. Initially 37 variables presumed to affect reindeer productivity were derived, quantifying variation in topography, climate, snow conditions, insect harassment, vegetation, forage abundance and qualities, and fragmentation of the ranges. A method was proposed, termed ‘reachability’, to quantify in cost-benefit terms the available grazing resources in relation to infrastructural fragmentation. The range-related variables were mapped for the entire reindeer husbandry area on a raster scale of 100 km2. The 37 variables were reduced to 15 using stepwise principal component analyses. These were thereafter used for characterisation of the reindeer herding ranges and for zonation of the Swedish reindeer herding area into seven zones. Furthermore, the 51 reindeer herding districts were divided into 10 groups with the help of cluster analyses. The remaining 15 variables were also related to productivity on the herding-district level with the help of canonical correlation analyses and structural equation modelling, in order to identify the important productivity determining factors, both in spatial and temporal scales. Larger variation in productivity was found between herding districts than between years. Different variables were found important for the between-district and within-district productivity variations, where season lengths and animal densities were significant at both scales. Other important factors were terrain ruggedness, insect harassment, calf slaughter and animal condition previous year. Snow conditions, disturbances and forage quality were not found to have large impact on productivity on this scale. These factors, however, may have been counteracted by husbandry measures and were therefore not easily detectable in relation to slaughter statistics. The most important environmental factors affecting reindeer productivity were used to suggest an ultimate grouping of herding districts into seven administrative groups for administrative planning and management purposes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".