Geologic Mapping Forum 22/23 Abstracts
Bibliographic record
Abstract
The Geologic Mapping Forum (GMF) in Minneapolis in 2018 and 2019 was attended by ~100 geological map authors, program managers and allied professionals from geological surveys and associated agencies, who met to discuss the status and future of geological mapping in the USA. Due to the coronavirus pandemic, the 2020 Geologic Mapping Forum planned for April 7th to 9th, 2020, was not held. It therefore was decided that GMF Online would be held as meetings of about two hours duration on Thursdays at Noon Central, about once per month, from early autumn until spring, in 2020/2021, 2021/2022, 2022/2023, and 2023/2024. A similar format is planned for 2024/2025. For the 2025/2026 GMF, plans are being developed for a September 2025 in-person meeting in Minneapolis, similar to the 2018 and 2019 meetings. Online meetings would follow, that winter. Early plans for 2026/2027 call for the in-person meeting to be in Denver. The intended GMF audience is geological map authors and program managers, and the focus in geology rather than funding or GIS. Optional abstracts for the 20-minute invited talks were requested, and are presented here.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.439 | 0.272 |
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".