AgDataBox API – Integration of data and software in precision agriculture
Bibliographic record
Abstract
Precision agriculture (PA) is a set of techniques of agricultural management that, with the use of information and communication technology, considers the spatial and temporal variability in the fields with regard to soil, atmosphere and plants for the best management of the crops, seeking to obtain the best result according to its potential. For a better performance of PA it is necessary to obtain information quickly and safely, therefore computational technologies have been applied to different crops in various countries. Software has been designed to solve specific problems by allowing the integration of computational applications for use in the agricultural environment. Such software makes an important contribution to farmers and researchers, allowing a deep analysis of agricultural data. The present study aims to develop a computational web tool that allows the storage, integration and management of agricultural data through specialized software. Through the Internet and HTTP requests/responses, the tool provides an interface that other software packages can use to send and receive different types of agricultural data (spatial and non-spatial), thus integrating multiple applications. It offers several advantages, specifically reducing application development time and integration. Such applications can be developed in different programming languages and used in different environments. As one example, two types of software (one mobile and another web) were integrated using this computational tool.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.021 |
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".