Understanding soil fertility status in Newfoundland from standard farm soil tests
Bibliographic record
Abstract
Farm soil tests are common decision support tools employed by regulatory agencies and farmers to manage nutrients in an economical and environmentally sustainable way. The complex interplay between the local environment and locally relevant crops makes soil testing, and critically soil-test-based recommendations, site-specific. Newfoundland and Labrador has a relatively small but rapidly growing commercial agriculture industry, mainly on lands converted from the boreal forest over the last 80 yr. A first step towards developing locally calibrated fertilizer recommendations is understanding current practices. For this, we examined regular farm soil test reports and associated recommendations for Newfoundland (Nfld). Following a request distributed to 167 farmers, 1503 soil tests were obtained from 32 farms. Although tests exemplify the gamut of crops in Nfld, more than half were from forage and mixed forage fields in western Nfld, representing dairy farms. Results show that even in the absence of more comprehensive site analyses, an investigative survey of farm tests may be employed to recognize possible environmental and economic inefficiencies of local cropping systems, including regional and crop type-driven differences for both nitrogen (N) and phosphorus (P) fertilizations. Soil-test-based identification of possible N and (or) P inefficiencies and associated crop and regional particularities, including excess fertilization, can be employed to devise targeted research for improved, preventative decision tools to increase the sustainability of Nfld agricultural systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".