INNOVATIVE METHODOLOGY FOR DETECTION OF FRACTURE-CONTROLLED SWEET SPOTS IN THE NORTHERN APPALACHIAN BASIN
Bibliographic record
Abstract
In the structure task, the goals for this reporting period were to: (1) complete field work on the NNW-SSE transect along the west side of Cayuga Lake; (2) collect data at additional field sites in order to (a) trace structural trends between the two N-S transects and (b) fill in data gaps on the NS transect along the eastern shore of Seneca Lake; (3) enter the data gathered from the summer field work; (4) enter data from the previous field season that still had to be analyzed after a personnel change. We have completed data reduction for all the goals listed above, including the NNW-SSE transect on the west side of Cayuga Lake. In the soil gas task, the goals for this reporting period were to: (1) trace Trenton/Black River fault trends between the two N-S transects; and (2) enter the data gathered from the summer field work. We have completed data reduction for all the goals listed above, and have begun constructing maps that portray the data. These data continue to demonstrate that integration of aeromagnetic and Landsat lineaments, surface structure, soil gas and seismic allows us to extrapolate Trenton-Black River trends away from confirmatory seismic lines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".