Developing and Applying a Protocol for Long-Term Monitoring at Local Natural Areas
Bibliographic record
Abstract
Access to authentic research is limited at the 7–12 science education level. At the same time, many local restoration projects would benefit from, but don’t have access to a long-term system of monitoring. This project seeks to unite those two needs by developing a protocol for 7–12 classrooms to be able to participate in authentic research through long-term monitoring of a local restoration project. The protocol developed in this project was used by Jenison High School students at Grand Ravines Park. Grand Ravines Park is a recently acquired Ottawa County park with a history of anthropogenic disturbances. Shortly after the acquisition of the final section of the park, Ottawa County Parks and Recreation (OCPR) staff seeded the portions of the north side of the park with native grasses and forbs, and the south side of the park was not supplementally seeded. This study aimed to develop protocols for the student collection and analysis of vegetation and invertebrate biodiversity data in both the north and south sides of the park. Various metrics of plant and invertebrate biodiversity were compared between the seeded and unseeded areas. Students from Jenison High School were involved in the data collection and analysis, increasing their exposure to scientific research and field sampling and developing their scientific literacy. A long-term database was developed, with the goals of facilitating park management decisions and driving future student research questions.
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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 | 0.094 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.026 |
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".