Vyhodnocení odrůstání výsadeb introdukovaných dřevin na LS Vítkov - TZP 3 Červený kopec
Bibliographic record
Abstract
This bachelor's thesis is intended to present the problems of tree species introduc-tion and to evaluate the success of the growth of introduced tree species planted in the research area Červený kopec. The site is located near the village of Dvorce in the Mo-ravian-Silesian Region in the Vítkov Forest Administration. The origin of studied tree species are from different parts of the world, or the species are different in their prov-enance. The tree species I studied in the research plot are Quercus cerris, Quercus ru-bra, Sorbus domestica, Quercus frainetto, Platanus hispanica, Gleditsia tricanthos, Corylus colurna, Castanea sativa, Carya cordiformins, Pinus nigra, Pinus ponderosa, Pseudotsuga menziesii, Pseudotsuga menziesi var. glauca, Abies borisii, Abies cepha-lonica, Thuja plicata, Picea engelmanni, Pinus peuce, Abies concolor var. lowiana, Abies concolor, Calocedrus decurrens, Chamaecyparis lawsoniana and Tsuga hetero-phylla. Measured data were collected from only a portion of the study area, namely from squares 1-32, 79, 80. Data were measured in 2023 in the spring period before the start of the growing season and then in the autumn period after the end of the growing season. Subsequent processing and analysis of the data by statistical tests indicated that the tree species that fared best in the study area were black pine p1 (Pinus nigra) from Bilace province in Kosovo, black pine p2 (Pinus nigra) from Divača province in Slovenia, heavy pine p2 (Pinus ponderosa) from Woodwark Creek province in Canada, heavy pine p1 (Pinus ponderosa) from Pritchard Province in Canada, Douglas-fir p2 (Pseudotsuga menziesii) from Caycuse River Province in Canada and Grey Douglas-fir (Pseudotsuga menziesii var. glauca) from Orchard Lake Province in Canada. The least prosperous species here were Hungarian oak (Quercus frainetto), three-thorned dog-wood (Gleditsia triacanthos) and giant arborvitae (Thuja plicata).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".