Precision agricultural data and ecosystem services: Can we put the pieces together?
Bibliographic record
Abstract
Abstract Ecosystem services can maintain or increase crop yield in agricultural systems, but data to support management decisions are expensive and time‐consuming to collect. Furthermore, relationships derived from small‐scale plot data may not apply to ecosystem services operating at larger spatial scales (fields and landscapes). Precision yield data (PYD) can be used to improve the accuracy and geographic range of ecosystem service studies but have been underused in previous studies: out of 370 literature records, we found that less than 2% of all records were used to study biotic or landscape effects on yield. We argue that this is likely due to low data accessibility and a lack of familiarity with spatial data analysis. We provide examples of analysis using simulated PYD, and outline two case studies of ecosystem services using PYD. Ecologists and agronomists should consider using PYD more broadly, as it can be used to test hypotheses about ecosystem services across multiple spatial scales, and could be used to inform the design of multifunctional farming landscapes.
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 | 0.050 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".