Hydrate Research‐From Correlations to a Knowledge‐based Discipline: The Importance of Structure
Bibliographic record
Abstract
Abstract: This contribution gives a short historical perspective on the development of fundamental knowledge about gas hydrates, and how such fundamental knowledge is important for a variety of problems related to hydrate prevention (pipelines), hydrate formation (CO2 sequestration), or hydrate decomposition (permafrost or marine natural gas hydrates). It is shown that early correlations derived from measurements on hydrates, many of which were made as early as the 1800s, could not be understood properly until around 1950 when X‐ray diffraction measurements gave a structural understanding of the materials we now know as clathrates, or host‐guest materials. In turn, this led to a statistical mechanical description and a thermodynamic model that has considerable predictive power. Recently, it has become apparent that there is considerable complexity and subtlety in the relationship between structure and the size of the hydrate formers once more than a single species is present. This emphasizes the need to obtain structural information together with thermodynamic measurements, especially in multicomponent systems. Such developments have encouraged the adoption of additional structural techniques such as NMR and vibrational spectroscopy. Current interest in hydrate formation and decomposition requires the adaptation and development of new approaches that allow the acquisition of time‐resolved structural information. Furthermore, an understanding of the morphology of hydrate crystals and how to modify this morphology, as well as understanding of interfacial properties, are all key to learning how to control hydrates. Although a great deal of progress has been made, much remains to be learned.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".