Field Analysis for the Prince George’s County Department of Parks & Recreation
Bibliographic record
Abstract
Maryland-National Capital Parks and Planning Commission (MNCPPC) – Prince George’s County is seeking to improve maintenance practices on its sports fields in order to provide safe, agronomically sound play areas for county residents and amateur sports teams. In coordination with the University of Maryland Program for Active Learning and Sustainability (PALS), the county sought to have a “turf inventory” conducted on its “rectangle” (primarily used for soccer, football, and lacrosse) recreational sports fields. This survey involved using the Sports Field Managers Association Playing Condition Index (PCI). The PCI is compiled using data from a number of qualitative and quantitative field parameters including the following: • Primary use, field manager experience, general field maintenance practices, and construction infrastructure (this portion of the rubric was answered by PG Parks) • Species of turfgrass and turfgrass cover • Species of weeds present and weed cover • Visual evaluation of the soil profile to 5-6” • Surface hardness measurements using the Clegg impact hammer. • Soil volumetric moisture content using a Field Scout TDR Moisture Meter. • Compaction levels measured with a Field Scout penetrometer. The primary objective of this survey was to provide PG Parks turfgrass management staff a characterization of the fields with regards to player safety and field conditions related to agronomic practices and field usage. The intention is that the information from this survey will be used soon to allocate maintenance resources to provide safe, high quality playing fields.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".