Nano-Scale Secondary Ion Mass Spectrometry - A new analytical tool in biogeochemistry and soil ecology
Bibliographic record
Abstract
Soils are structurally heterogeneous across a wide range of spatio-temporal scales. Consequently, external environmental conditions do not have a uniform effect throughout the soil, resulting in a large diversity of micro-habitats. It has been suggested that soil function can be studied without explicit consideration of such fine detail, but recent research has indicated that the micro-scale distribution of organisms may be of importance for a mechanistic understanding of many soil functions. Due to a lack of techniques with adequate sensitivity for data collection at appropriate scales, the question 'How important are various soil processes acting at different scales for ecological function?' is challenging to answer. The nano-scale secondary ion mass spectrometer (NanoSIMS) represents the latest generation of ion microprobes which link high-resolution microscopy with isotopic analysis. The main advantage of NanoSIMS over other secondary ion mass spectrometers is the ability to operate at high mass resolution, whilst maintaining both excellent signal transmission and spatial resolution ({approx}50 nm). NanoSIMS has been used previously in studies focusing on presolar materials from meteorites, in material science, biology, geology and mineralogy. Recently, the potential of NanoSIMS as a new tool in the study of biophysical interfaces in soils has been demonstrated. This paper describes the principles of NanoSIMS and discusses the potential of this tool to contribute to the field of biogeochemistry and soil ecology. Practical considerations (sample size and preparation, simultaneous collection of isotopes, mass resolution, isobaric interference and quantification of the isotopes of interest) are discussed. Adequate sample preparation avoiding biases in the interpretation of NanoSIMS data due to artifacts and identification of regions-of interest are of most concerns in using NanoSIMS as a new tool in biogeochemistry and soil ecology. Finally, we review the areas of research most likely to benefit from the high resolving power attainable with this new approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".