INTEGRATING REMOTE DIGITAL TOOLS INTO POST-PANDEMIC GEOLOGIC FIELDWORK TO EFFECTIVELY DISSEMINATE CONTENT DELIVERY AND ASSIST IN OVERALL UNDERSTANDING OF VARIOUS GEOLOGIC PHENOMENA: SUMMER 2022 FIELD MAPPING EXERCISES
Bibliographic record
Abstract
INTEGRATING REMOTE DIGITAL TOOLS INTO POST-PANDEMIC GEOLOGIC FIELDWORK TO EFFECTIVELY DISSEMINATE CONTENT DELIVERY AND ASSIST IN OVERALL UNDERSTANDING OF VARIOUS GEOLOGIC PHENOMENA: SUMMER 2022 FIELD MAPPING EXERCISES SHAMI, Malek, BETHEL, Cherise, NUNEZ, Eddy, RAYHAN, Salam, KHANDAKER, Nazrul and CABAROY, Charren C. Geological Society of America Abstracts with Programs. Vol 54, No. 5, https://doi.org/10.1130/abs/2022AM-379779 SHAMI, Malek1, BETHEL, Cherise2, NUNEZ, Eddy3, RAYHAN, Salam3, KHANDAKER, Nazrul2 and CABAROY, Charren C.1, (1)Geology Discipline, AC-2F09, York College of CUNY, 9420 Guy R. Brewer Blvd., Jamaica, NY 11451, (2)Geology Discipline, York College of CUNY, 9420 Guy R Brewer Blvd, AC-2F09, Jamaica, NY 11451-0001, (3)School of Earth and Environmental Sciences, CUNY Queens College, . 65-30 Kissena Blvd., Flushing, NY 11367 In the summer of 2020, a case study titled “Integrating Digital Tools in Remote Learning to Enhance the Delivery Methods of Technical Content in Undergraduate Geosciences” was virtually presented at the GSA annual meeting (initially scheduled in Montreal, Canada). The primary objective concerning the usage of digital tools was to highlight the abrupt COVID-19 induced transition to remote learning, the subsequent impact on geologic fieldwork, and the deployment of new digital tools as an adaptation to the unprecedented change in the learning environment. Here, authors discuss integrating such digital tools and lessons learned from geologic fieldwork conducted during the pandemic into the post-pandemic geologic field investigation. The summer 2022 Geologic Field Mapping Course (capstone course) conducted in Rosendale, Ulster County, NY, involved lower to mid Paleozoic complexly folded siliciclastic and carbonates and was taught by adhering to pre-pandemic standards. Students from both the City University of New York (CUNY) York College and CUNY Queens College had the opportunity to camp in the field using facility provided by North-South Lake for the entire duration of the course. Accommodation near the point of interest was possible largely due to easing of social distancing protocols and being closer to Rosendale, enabled students to inspect outcrops for field data collection. Digital tools carried over from the 2020-2021 pandemic era included the employment of a 5G Internet Hotspot, a miniprojector, and the use of various remote software such as DPlot, Sedlog, ArcPro GIS, and Google Earth. Pertinent lithologic and structural data were plotted to draw cross-sections, correlate outcrops/units, and decipher depositional environments of the exposed sedimentary rocks. The outcomes of this recently concluded field mapping exercises demonstrate that integrating lessons learned and utilization of digital tools not only optimize geologic fieldwork, rather, it also enhances the efficiency of statistically analyzing data, making real time decisions in the field, and correlating various findings to previously published academic literature. Access to 5G Internet Hotspot in remote setting became very effective in terms allowing students to gather peer-reviewed geologic information and minimize the knowledge gap, if any. Sunday, 9 October 2022: 9:00 AM-1:00 PM Exhibit Hall F (Colorado Convention Center)
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".