Practical applications of ion exchange resins in agricultural and environmental soil research
Bibliographic record
Abstract
The use of synthetic ion-exchange resins to examine ion bioavailability in soil and sediment systems has attracted much attention over the years. The first report in this regard was made 7-8 yr after resins were developed in the 1930s. So far, nearly 400 journal articles have been published related to use of resins in soil and environmental studies. The experience gained has led to more widespread applications in research as well as practical use in soil fertility assessment and fertilizer recommendations. Two commercial products developed in North America have directly resulted from years of research efforts. Recent developments in resin technology and availability warrant an updated review of the literature to aid in better understanding and utilizion of this technique. In this paper we provide an overview of historic and current developments in the use of ion exchange techniques in soil research. We also provide specific examples of successful use of batch and diffusion-sensitive ion exchange techniques in research and commercial use to assess ion availability. Finally, we address certain frequently asked questions about how the ion exchange resin technique is applied and how results are interpreted, including their advantages and limitations. Key Words: Ion exchange resin, agriculture, environment, soil research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".