ISOTHECIUM MYOSUROIDES AND THUIDIUM TAMARISCINUM MOSSES AS BIOINDICATORS OF NITROGEN AND HEAVY METAL DEPOSITION IN ATLANTIC OAK WOODLANDS
Bibliographic record
Abstract
Moss tissue chemistry is widely used as a bioindicator of atmospheric deposition. The objective of this study was to compare the tissue chemistry of two moss species in Irish Atlantic oak woodlands, Isothecium myosuroides [Im] and Thuidium tamariscinum [Tt], and to determine their relationship to indices of atmospheric deposition. Moss species were collected from twenty-two woodland sites during April 2013 and analysed for nitrogen, sulphur, and eleven heavy metals. Nitrogen content was significantly correlated between species (rs = 0.84), but their mean values (Im = 1.23%, Tt = 1.34%) were significantly different. A simple linear regression suggested that nitrogen content was significantly related to atmospheric ammonia (R2 = 0.67 [Im], R2 = 0.65 [Tt]) and total nitrogen deposition (R2 = 0.57 [Im], R2 = 0.54 [Tt]). Many heavy metals had significant interspecies correlations (Al, V, Ni, Cu, Zn, As, Sb, Pb; rs = 0.46−0.77). A few metals (As, Sb and Pb) were positively correlated with easting and northing for both species, which may suggest transboundary or national industrial emissions sources. The results suggest that both species could be used as bioindicators of deposition for nitrogen and some heavy metals, although further study of the relationship between tissue concentrations and atmospheric deposition is warranted. Furthermore, interspecies calibration is required to use both species in conjunction.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".