Revisioning and reinvigorating a hydrogeology distinguished lecture tour through learner-engaged workshops
Bibliographic record
Abstract
Abstract As geoscience and water related enrollment and degrees continue to decline, new methods are needed to recruit and engage students in these interesting and challenging interdisciplinary fields. An existing water-themed distinguished lecture tour was reinvented and reinvigorated to include a workshop to (1) promote interdisciplinary collaboration and (2) increase student engagement in earth sciences. The Geological Society of America has dedicated foundation support for a domestic and international lecture tour to promote the scientific discipline of hydrogeology. In addition to the lecture tour with a standard presentation and meetings with faculty, staff, and students, a half-day, interactive field demonstration of the inventory and assessment techniques for springs ecosystems was conducted at a spring near the lecture site. Local faculty hosts were encouraged to engage students in the planning and implementation of the demonstration workshop. The students were provided additional training to enter the interdisciplinary ecosystem data into a cloud-sourced database and to be actively engaged in producing a publication related to the springs ecosystems inventories and assessments. Of the participants of the demonstration workshops, students at eight of the host institutions became co-authors of the resulting manuscript. With the significant investment of time, resources, and logistics to implement a lecture tour, an additional interactive, hands-on, demonstration workshop is a cost-effective way to increase student engagement and provide interdisciplinary collaboration.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".