An Automated Template Approach for Generating Web‐Based Conservation Planning Worksheets
Bibliographic record
Abstract
An automated web‐form generator system was created to rapidly produce dynamic web‐based inventory and assessment worksheets that are used by the USDA Natural Resources Conservation Service (NRCS) to describe the condition of natural resources on farms and ranches. These worksheets are used by NRCS as part of the criteria for creating a conservation plan to qualify landowners for payments under the USDA Farm Bill Conservation Title. Presently, most worksheets are filled out by hand or in personal computer spreadsheets. If worksheets such as these were available over the web, landowners could also self‐assess the conditions on their farms or ranches before requiring assistance of NRCS conservation planners or technical service providers. Because many conservation worksheets have a similar style and behavior, we created an automated web‐script generator that produces a dynamic web‐page worksheet from a common template file and a configuration file. A worksheet‐specific configuration file provides the details for the worksheet. The template, configuration files, and web scripts are all in PHP (PHP hypertext processor). Each configuration file required approximately 5 h to program, a savings of more than 4 d compared with a web‐based worksheet programmed individually with a scripting language. In this way, various conservation worksheets can be quickly created for the web, as well as be easily maintained. As an example, we produced a suite of assessment worksheets used by Oregon NRCS with our automated web‐form conservation worksheet system. Those worksheets are accessible at: http://yukon.een.orst.edu/ms_apps/wq_indexes/forms/index_menu.html (verified 5 May 2006).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".