Distribuce a početnost bobra evropského v lužních lesích v okolí soutoku Moravy a Dyje
Bibliographic record
Abstract
The bachelor's thesis focuses on obtaining current data on the abundance of Eurasian beaver (Castor fiber L.) in the European important locality Soutok - Podluží, where this animal has long been present and there is an interest in increased protection. Field data collection took place in the first quarter of 2020. The area around watercourses and water bodies with a total length of 348.15 km was monitored. All residence sings that were in the locality of interest were registered. Subsequently, the data in the GIS environment were analyzed and evaluated. The output was the demarcation of territorial boundaries, and in the next step the calculation of a rough estimate of the number of beaver populations in the selected area. Based on these data, the density of the area and the occupancy of the environment were further evaluated. From the collected data, it was also possible to determine the proportions of consumed tree species, including their preferred average. The results of the survey showed that the number of territories in EVL Soutok has increased from 65 to 80 since the last monitoring in 2018.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.068 | 0.017 |
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".