Program “RCLC”: Garnet–Orthopyroxene Thermobarometry Corrected for Late Fe–Mg Exchange
Bibliographic record
Abstract
A web-based version of program “RCLC”, the software used in the above paper, is now available at the following links: https://rclc.facsci.ualberta.ca/ https://www.ucalgary.ca/pattison/rclc RCLC, which is short for ‘recalculation’, is a program that calculates pressure–temperature (P–T) conditions of garnet–orthopyroxene–plagioclase–quartz±cordierite±biotite (Grt–Opx–Pl–Qtz±Crd±Bt) mineral assemblages. It is based on Al-solubility in Opx in equilibrium with Grt, corrected for late Fe–Mg exchange. The rationale and calculation method for the program are described in Chacko et al. (1996) and in Pattison et al. (2003). Figure 1 (figure 3 from Pattison et al. (2003)) shows graphically how RCLC works. The program described in Pattison et al. (2003) was written in BASIC and compiled on a PC. However, because the BASIC language is no longer compatible with newer versions of the Windows operating system, the RCLC program was converted into a Web-based application in which the underlying computer code is written in the PYTHON programming language. RCLC was originally written by Tom Chacko and James Farquhar in 1996 and subsequently modified by Chris McFarlane, David Pattison and Tom Chacko between 1997 and 2002. The Web-based version of RCLC was developed by Justin Widney of the University of Alberta in 2018 as part of an undergraduate summer internship under the supervision of Tom Chacko.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.021 |
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".