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
Development of agriculture and the need for better environment require more soil samples being analyzed in shorter time for soil nutrient information, as a result, modernization of soil nutrient analysis is the way we must choose. This paper reviewed progress in application of universal extractants (such as AB-DTPA, 0.5 mol/L sodium bicarbonate, 0.01 mol/L calcium chloride, Kelowna, strontium-citric acid solution and water), application of instrumental analysis (such as flow analysis, capillary electrophoresis, liquid chromatography, atomic emission spectrometry and electrode) for soil nutrient analysis, and automation of soil sample collection and analysis in the last 20 years. However, mixing of soil sample and soil moisture determination are still two problems in the automation of soil nutrient analysis. To compromise between the analysis speed and the accuracy is recommended as the solution.
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.
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 | 0.000 | 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 | 0.000 | 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 it