Bibliographic record
Abstract
Our current issue features a whole series of fantastic work, including the award-winning papers of the Fall 2023 DFI student paper competition. The issue begins with a Canadian-based author team who present ‘Performance Evaluations of SPT-based Design Methods for the Axial Capacity of Driven Piles in Glacial Deposits’. Jon Sinnreich (GRL) follows up with a novel introduction of Tangent Bearing Elements (TBEs). His analysis of several bi-directional static load test programs conducted on TBEs provides clarity for designers and contractors. Other authors featured in this issue include Pedram Roshani, Julio Ángel Infante Sedano, Mohammad Amin Tutunchian, Reza Rezvani, Markus Jesswein, Jinyuan Liu, Minkyung Kwak, L. Huang, A. Martinez, C. Aubeny, D. DeGroot, S. Arwade, R. Beemer,Md Kausar Alam and Ramin Motamed.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.042 | 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".