Characterization of soil magnetic susceptibility: a review of fundamental concepts, instrumentation, and applications
Bibliographic record
Abstract
The characterization of magnetic susceptibility (MS) has become an accepted technique in soil science. This review examines the concept of volume and mass-specific MS, magnetism, frequency dependence, and thermal behavior of MS, as they pertain to soil material. A comparison is presented of the two types of instrumentation for measuring soil MS, based on magnetic field and electromagnetic induction (EMI). These are discussed with respect to applications including magnetic granulometry, detection of pollutants, identification of organic matter, the delineation of drainage class, paleo-environmental studies, archaeology, as well as soil erosion and degradation. Instruments that use magnetic fields can precisely measure the MS of small amounts of soil, thinly deposited layers or soil exposures, but cannot effectively measure materials at distances ≥10 cm from the sensor. EMI instruments, instead, are capable of quickly measuring apparent MS of a finite volume of the soil, and are utilized in mapping of soil MS in agricultural and archaeological investigations; however, the measured apparent MS values need to be further processed to give the real volume MS values of soil layers/segments. Although both kinds of instruments are widely used in soil science, their measured data are not interchangeable. Future work should be conducted to increase the understanding of the comparability of these instruments to find better utility among soil scientists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".