Bibliographic record
Abstract
Technology Focus Past articles for this feature have touched on the topic of decision making. Indeed, last year’s article tackled the approach to decision making with a focus on situational awareness. This year, we focus on the availability of knowledge as an aid (on the one hand) and a prerequisite (on the other) to our ability to execute decisions rapidly and correctly. Distilling the key paper topics offered up for this year’s Knowledge Management and Training feature resulted in an interesting “triple-S” configuration of systems, state, and studies. Systems. Knowledge-base (KB) systems are becoming a key component in the workflow for delivering critical decision making. The industry has transcended what may now be considered standard databases. A variety of database configurations is available commercially, with great advances made over recent years. Overlaying available data with information and carefully collated knowledge is an emerging practice. Delivering the KB within a spatial frame of reference, commonly a geographical-information system (GIS), provides for a rich degree of multidimensionality. State. Continuous access to the “state” of the integrated asset, including the reservoir, production, and pipeline systems, is readily available by use of now-commonplace supervisory control and data-acquisition systems. However, the ability to translate the state into meaningful information requires transfer through a series of process layers that filter and aggregate the data such that action may be taken as necessary and appropriate to maintain production targets. Studies. The industry invests significant capital in conducting detailed integrated studies of assets at various points in their life cycles. While keeping internal and external consulting organizations in business, these studies usually deliver an enhancement, depending on the degree of acceptance of the recommendations. Additionally, depending on the organization, the shelf life of these studies may be somewhat limited. Any approach is, therefore, welcomed if an integrated study is incorporated within an enterprise decision-making frame of reference. And the learning process continues…. Knowledge Management and Training additional reading available at OnePetro: www.onepetro.org SPE 145080 • “Reservoir Engineering for Unconventional Gas Reservoirs: What Do We Have To Consider?” by C.R. Clarkson, University of Calgary, et al. SPE 144321 • “Integrating All Available Data To Improve Production in the Marcellus Shale” by Efe Ejofodomi, Schlumberger, et al. OTC 21491 • “GIS Development for Geophysical- and Geotechnical-Data Integration: Application to West Africa Geohazard Assessment” by G. Dan-Unterseh, Fugro France SAS, et al. OTC 21538 • “GIS Technology Development for Sediment Characterization of Angolan Deepwater Soil Conditions” by M. Hamon, Angolan Deepwater Consortium (Doris Engineering), et al.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".