The IPIECA Marine Geospatial Bibliography
Bibliographic record
Abstract
Abstract Research on marine ecosystems is progressing rapidly, but information is often from disparate sources and in different formats. Although nominally publically available, it is frequently difficult to find, presenting a challenge in ensuring relevant information can be accessed freely and easily by all interested audiences. IPIECA, the global oil and gas industry association for environmental and social issues, have developed and launched an innovative knowledge sharing platform called the Marine Geospatial Bibliography (MGB). The business case for the development of the MGB is to centralise relevant environmental understanding relating to oil and gas activities in marine areas, ensuring it is transparent and can serve as the basis for policy and regulations, by making it publicly accessible whilst reducing duplication of effort. The MGB’s mission statement is to ‘‘identify and share sources of up-to-date scientific knowledge for management of oil and gas activities related to the marine environment, biodiversity, and ecosystems, whilst promoting ocean stewardship through increased transparency and greater awareness". The tool centralises and geospatially references reliable information, making it easy to find, search and summarise. The advantages of the MGB include that it: –Supports science-based environmental management enabling licence to operate;–Provides technical/scientific credibility and weight-of-evidence for regulatory decisions;–Allows horizontal transfer of knowledge between different communities of users;–Helps retain critical technical knowledge in the industry beyond the retirement boom The MGB’s primary audience is the oil and gas industry, however it is also intended to be of use to other audiences. The oil and gas industry often work with consultants who carry out baseline assessments or Environmental Impact Assessments (EIAs) so the information in the MGB could be relevant and useful for them. Additionally, it may be of interest to academics and scientists that perform research on deep water and marine environments. The MGB will also be of use to regulators and policy makers that make decisions based upon best available information to regulate the oil and gas industry. Finally the information contained in the MGB may be of interest to the public. At launch the MGB contained over 10,000 resources (including key papers, academic references and relevant industry grey literature) with an initial focus on the North Sea and Gulf of Mexico. This paper will explain the background/rationale for the project, the functionality and applicability of the MGB, and provide additional insight into the resources available. The paper will illustrate how IPIECA members are contributing information to the tool in the interests of greater transparency, and will outline future plans for expansion.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.087 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.143 | 0.082 |
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".