Elemental Composition of Reindeer Pasture Plants and Lichens in Nadym District (Yamal-Nenets Autonomous Area)
Bibliographic record
Abstract
Abstract—There are plans to expand reindeer husbandry in the Nadym District of the Yamal-Nenets Autonomous Area. For this purpose, we studied the elemental composition of the dominant species of the tundra and open boreal woodland vegetation cover. We analyzed leaves of dwarf birch (Betula nana L.), dwarf shrubs of bog blueberry (Vaccinium uliginosum L.), marsh Labrador tea (Ledum palustre L.), and leatherleaf (Chamaedaphne calyculata (L.) Moench); sphagnum moss (Sphagnum sp. L.); and fruticose lichens (Cladina stellaris (Opiz.) Brodo). The X-ray fluorescence analysis was used to obtain data on the content of Ca, K, P, Si, Mg, Na, S, Zn, Cu, Ni, Co, Fe, Mn, Cr, Ti, and Al. We defined the biogeochemical features of the reindeer forage plants. In vascular plants and sphagnum mosses, the content of almost all essential macroelements is low, while the content of most microelements (Cu, Ni, Co, Cr, and Mn) exceeds the world average values. The lichens are characterized by low concentration of Ca, K, Mg, and P, which is more than one order of magnitude lower than the world average values, and the deficiency of microelements. The results were compared with the results from similar studies in other geographical regions of the tundra zone, and it was found that tundra plants have a similar pattern of element accumulation. In particular, leaves of dwarf birch are distinguished by accumulation of Mg; the content of Al, Fe, and Si is increased in mosses; Mn is accumulated in dwarf shrubs and dwarf birch; lichens are characterized by the deficiency of most elements. Therefore, in order to prevent animal diseases, it is necessary to improve the elemental composition of reindeers feed by increasing the share of “green” forage in winter, when lichens dominate the diet.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".