Bibliographic record
Abstract
The Queensland Geotechnical Database (QGD; qgd.org.au) was launched in October 2017 with the aim of consolidating primarily tax and toll-payer subsidised geotechnical investigation logs into an open platform. The QGD was influenced by public geotechnical databases in the United Kingdom and New Zealand, and the work of Robert Leggett in Canada as summarised in ‘Cities and Geology’ (1973). As of October 2023, the QGD includes over 3100 geotechnical investigation logs authored by over 10 public and private entities, dating back to 1966. It also includes national geological mapping and links to over 400 technical papers related to sites in Australia. This paper summarises the formation of the QGD, which emerged from the Queensland Chapter of the Australian Geomechanics Society (AGS) and originated from a personal database converted to an open format with hosting support from The Open Data Institute Australia. The QGD was later transferred to The University of Queensland and continues there in support of their Sustainable Infrastructure Research Hub (UQ SIRH). The paper explores the evolution of its formation, the legal framework in Australia regarding investigation log ownership, and the licensing scheme adopted for the database. It outlines the technical features and intended practicality of the database, and its alignment with the objectives of the UQ SIRH. The paper concludes with an outline of opportunities for conversion to a nationalised Australian Geotechnical Database and its usage for educational purposes.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.170 | 0.124 |
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".