The Scientific Induced Seismicity Monitoring Network
Bibliographic record
Abstract
The SM Network will be used to collect data from the industry deployed seismic stations within the province of Alberta, Canada. The network will be set-up like and ingest data from a currently operating temporary 2K network. The stations will mostly utilize Nanometrics equipment, although as the network expands, other vendors might deploy their own equipment. This network will need to have a year-long embargo on the collected data. The station data will be submitted by the vendors, but the AGS employees should be official custodians of the network and in term have a full access to the data. 2025-02-24: The Scientific Induced Seismicity Monitoring Network (currently designated under 2K) is in the process of moving to the new network code I0 [https://ds.iris.edu/mda/I0/]. During this process, the data will be fragmented between both networks and interested users are asked to check both networks for the required data. Once the data is fully moved to the I0 designation, the 2K will be closed. For additional information or assistance, please email Javad Yusifbayov (javad.yusifbayov@aer.ca)
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.039 |
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".