Environmental ISR (Quarterly Progress Report Q1 FY20 Oct-Dec. 2019)
Bibliographic record
Abstract
Progress has been good on both projects. Four of us (Annie Kersting, Mavrik Zavarin, Gauthier Deblonde, and Mike Homel) attended a yearly workshop held at the Ben Gurion University in Israel to discuss last year’s progress and FY20 workscope. Dates for our meeting were Nov. 11-13th, 2019. A significant effort this quarter was spent on developing the workshop agenda, travel logistics, and preparing presentations, and attending the workshop. A draft summary of the workshop is attached at the end of this report. Also attached are our two powerpoint presentations. The workshop was very successful and ended with a ½-day fieldtrip to the proposed borehole site and surrounding geology. We made significant progress discussing next year’s scientific agenda. MW3:Thermal, hydrologic and mechanical modeling: Mike Homel helped finish a co-authored manuscript entitled, “Breakout modeling in arkose and granite rocks, that should be submitted for review in Jan-Feb, 2020. MW2: Radionuclide transport in carbonate: Graduate student ,Emily Tran, presented a poster at the American Geophysical Union meeting in San Francisco, Dec. 10th, 2019, of work that was also presented at our workshop in Israel. Her poster session was well attended. Annie Kersting met with Emily to also discuss next year’s publications and workscope. Report includes posters and slides.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.428 | 0.332 |
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".