A critical examination of crop-yield data on vegetables, maize (Zea mays L) and tea (Camellia sinensis) for Sri Lankan biofilm biofertilizers
Bibliographic record
Abstract
Abstract With increasing global interest in microbial methods for agriculture, the commercialization of biofertilizers in Sri Lanka is of general interest. The use of a biofilm-biofertilizer (BFBF) commercialized in Sri Lanka is claimed to reduce chemical fertilizer (CF) usage by ~ 50% while boosting harvest by 20–30%. Many countries have explored the potential of biofilm biofertilizers, but have so far found mixed results. Here we review the BFBF commercialized in Sri Lanka and approved for national use. We show in detail that the improved yields claimed for this BFBF fall within the uncertainties (error bars) of the harvest. Theoretical models that produce a seemingly reduced CF scenario with an “increase” in harvests, although this is in fact not so, are presented. While BFBF usage seems to improve soil quality in some instances, the currently available BFBF promoted in Sri Lanka appears to have negligible impact on crop yields. We also briefly consider the potentially negative effects of large-scale adoption of microbial methods. The manner whereby a poorly-tested but product of biotechnology gained government and institutional acceptance is of global relevance in the rush to adopt new climate-mitigating technologies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".