Measuring lichen specimen characteristics to reduce relative local uncertainties for trace element biomonitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Local variation (within sampling sites) affects lichen air pollution biomonitoring of trace element deposition patterns. When comparing between sampling sites, global variation must be greater than local variation, thus reducing local variation is important in biomonitoring studies. To reduce local variability, sampling protocols are introduced, primarily minimum sampling height and less often sampling aspect. This study, introduces further protocols, which can help to reduce within site variation. First, the research design removed spatial variation by sampling a single tree. One–thousand and thirty–seven individual specimens of Usnea subfloridana were collected and aggregated into 97 samples based on similar collection height, aspect and mass. Samples were tested by inductively coupled plasma – atomic emission spectroscopy for total recoverable Al, As, Ba, Be, Cd, Co, Cr Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Sr, Ti, Tl, V, and Zn. Fifteen of the elements tested were above minimum detection limits and their variation in concentrations were able to be partially explained with linear modeling. When explaining variation in concentrations with linear modeling, aspect was statistically significant for all of the 15 elements, height was statistically significant for 12 elements, and specimen mass was significant for 6 elements. We demonstrate that individually assessing and minimizing specimen collection aspect, height and mass prior to aggregating specimens into samples can reduce local variation, which will improve between site comparisons.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it