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.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".