Canadian Dip-In DAS (CanDiD) Project 1
Bibliographic record
Abstract
The objective of the CanDiD-1 project was to acquire distributed acoustic sensing (DAS) data in a deviated wellbore during hydraulic fracturing operations in nearby horizontal wells, and thereby to characterize the subsurface strain and microseismicity that accompanied the growth of tensile fractures during well stimulation. The experiment took place in the Dawson Creek region of northeastern BC, Canada. As one of the first times that a temporary optical fibre was used for this purpose in Canada, a secondary technical aim of CanDiD-1 was to evaluate the effectiveness of a temporarily deployed fibre. The data were acquired using a DAS gauge length of 7.1 m and a spatial sampling interval of 1m, with a temporal sample rate of 1 kHZ. A fibre optic cable was deployed from the wireline truck and tractor to a measured depth of 4300m into a deviated well. The raw strain observations were acquired in a proprietary data format and have been converted to .hdf5 data format, which can be read using the open source package h5py (https://www.h5py.org).
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.049 |
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".