Accumulation of Minerals by <i>Leccinum scabrum</i> from Two Large Forested Areas in Central Europe: Notecka Wilderness and Tuchola Forest (Pinewoods)
Bibliographic record
Abstract
Abstract Leccinum scabrum sporocarps and associated topsoils from two areas in Poland have been characterized for contents and bioconcentration potential of Ag, Al, Ba, Ca, Cd, Co, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, P, Pb, Rb, Sr and Zn. Topsoil and fruitbody element composition varied between the two study sites, most likely as a result of local soil geochemistry. Element content of the labile fraction in topsoil from both sites followed the ‘pseudo‐total’ fraction and median values (mg kg−1 dry matter) were: K 380 and 340, Mg 760 and 840, P 1100 and 920, Al 3800 and 8100, Ag 0.31 and 0.28, Ba 28 and 37, Ca 920 and 790, Cd 0.23 and 0.23, Co 2.0 and 1.7, Cu 3.2 and 3.6, Fe 2800 and 6300, Mn 280 and 180, Na 99 and 110, Ni 7.8 and 8.8, Pb 12 and 18, Rb 1.3 and 2.1, Sr 4.8 and 4.0 and Zn 22 and 19, respectively. Only for some elements such as K, Mg, Al, Ag, Ca, Co, Mn, Na, Ni, Sr and Zn we found concentration differences between the two study sites for the caps of sporocarps. With the exception of Al, Mn, Na and Pb, stipes showed a similar tendency. Caps had a higher concentration of K, Rb, P, Mg, Al, Ag, Cu, Fe, Zn, Cd, Pb and Ni compared to stipes, while Na, Ba and Sr contents were higher in stipes. The comparison of soil and fruitbody concentrations indicates that L. scabrum bioconcentrate some elements while others are bioexcluded.
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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 | 0.000 | 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) | 0.001 | 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".