The average elemental composition of Canadian temperate climate vegetation
Bibliographic record
Abstract
The elemental composition of vegetation is poorly known so we report on the minor and trace element concentrations (~50 elements) in leaves and needles from the dominant species of woody vegetation at locations across Canada. Data are combined with estimates for elements near analytical detection limits, to yield an average composition for temperate climate vegetation, mostly grown on post-Pleistocene glacial soils approximating average continental crust (total 71 elements). Lithophile K, Mg, Ca, Sr, chalcophile Hg, Cu, Zn, Mo and Cd and siderophile Ag, Pd, Ir, and Os with low ionic charges and intermediate effective ionic radii show average vegetation/average continental crust ratios (hereafter vegetation/crust) above 0.1. Environmentally-sensitive, 2+, Cd and Hg are highly water-soluble and enriched in vegetation (2.4 and 1.0 * continental crust, respectively). Other high vegetation/crust ratios (1.0 to ~10) are shown by life-essential, 3+ B, 5+ P and 6+ S which have small ionic radii and thus, high effective ionic potentials. Elements with low ratios (~0.001) include the lithophile 3+ rare earth elements (REE, La to Yb), Al, Sc and Ga, 4+ Ti, U, and Th and 5+ V. However, 4+ Zr and Hf and 5+ Nb and Ta form a low concentration trough (~0.0001 * continental crust) in charge-radius space. These concentration patterns reflect charge and radius control on the solubility of elements in soil water. Assuming the immature soils approach average continental crust, the vegetation/crust ratios reflect the partitioning of elements between soil minerals and water taken up by plants. Organizing the elements from highest (S = 13) to lowest (Hf = 0.00004) ratios, allows normalizing other vegetation-based materials with average vegetation to decipher processes impacting the materials. Two published agrifood data sets for Canadian wines and maple syrups illustrate utility of the average vegetation data set for inferring processes.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".