Bibliographic record
Abstract
Fundamentals of thermodynamics, heat transfer, and fluid mechanics that are related to geothermal energy systems are provided to familiarize readers with these topics and prepare them for subsequent chapters. A good knowledge of thermodynamics is important to understanding geothermal energy, especially heat pumps. Facets of thermodynamics most relevant to geothermal energy systems and their applications are introduced and particular attention is paid to the quantity exergy and the methodology derived from it, exergy analysis. Aspects of heat transfer relevant to geothermal energy systems are introduced to provide the reader with a good grounding in heat transfer, which is central to geothermal energy utilization and its application. The three main modes of heat transfer are considered: conduction; convection; and radiation. A good grounding of fluid mechanics helps in understanding geothermal energy systems, as fluid flow problems often arise, so elements of fluid mechanics relevant to geothermal energy systems are also introduced. Finally, basic concepts about the ground are presented, since such material is fundamental to understanding ground-based geothermal systems, including information on ground temperature range and gradients, ground properties, and the existence of ground-based ecosystems and their sensitivity to human activity in the ground.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.126 |
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".