Bibliographic record
Abstract
Field research in soil science ranges from modal profile descriptions in support of soil survey to elaborate manipulative experimental designs. All of these field approaches make a valuable contribution to soil science, but researchers who do not use either classical manipulative experimental or geostatistical designs have little guidance (or encouragement) available to them. Well-designed field research of any type requires a clear definition of the research question; a thorough review of the literature to establish the state of knowledge; definition of the population under study and the elements that comprise it; and choice of appropriate scales for sampling support, spacing, and study extent based on an understanding of the underlying processes. For studies where hypothesis testing is appropriate, the hypotheses should be based on sound biological or physical reasoning, and sufficient replicates should be taken to ensure a reliable test. The major challenge in field research design is the development of landscape-scale research designs to examine complex interactions among hydrological, climatic, chemical, and biological processes at scales relevant for environmental management. Key words: Research design, landscape-scale , soil genesis, pattern studies, hypothesis testing, spatial statistics, sampling, cesium
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 | 0.188 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".