Bibliographic record
Abstract
Thermal conductivity The thermal conductivities of the main silicate minerals are well known and vary within a rather large range of about 1.6–7.7 W m -1 K -1 (Horai and Simmons, 1969; Diment and Pratt, 1988). The lowest and largest conductivity values are those of plagioclase and quartz, respectively. In principle, it is possible to calculate the thermal conductivity of a rock from knowledge of its mineral phases and their proportions. The procedure is discussed in a separate section below but is rarely implemented. Silicate minerals are anisotropic and belong to solid solutions with end-members that may have very different conductivities. An accurate prediction would thus require determination of the composition and orientation of each mineral phase. For heat flux measurements, furthermore, such determinations would need to be done over a representative rock volume and not at the scale of a petrological thin section. For this reason, thermal conductivity must be measured on each and every rock type encountered in a borehole. For regional thermal models, one may choose representative values for the dominant rock types, but one must pay attention to the level of approximation that is entailed. Table D.1 lists values for thermal properties of the main rock types and emphasizes the large ranges that exist for some of them. An excellent compilation of thermal conductivity measurements was made by Robertson (1988). Most of the available measurements have been made on upper crustal rocks and ignore deeper crustal lithologies.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.045 |
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".