Bibliographic record
Abstract
Description: This is a relatively major update from CO2SYS v3.1.1 to CO2SYS v3.2.0. It is designed to be maximally compatible with PyCO2SYS v1.7.0, a program for CO2 system calculations in Python that introduced a number of additional capabilities, improvements to performance, and fixes to minor errors/simplifications (Humphreys et al., submitted). Rigorous validation was performed against PyCO2SYS v1.7.0 as part of this update. Major changes since CO2SYS v3.1.1: 1) Initial pH estimates for iterative pH solvers are obtained via the approach of Munhoven (2013), detailed further in Humphreys et al. (submitted), rather than simply using an initial estimate of 8.0 each time. 2) Free scale pH is obtained properly within iterative pH solvers no matter the input scale, rather than making the simplification that input pH is always on the total scale. 3) Uncertainties in parameters calculated at output conditions that are associated with equilibrium constants are calculated calculated with respect to equilibrium constants at output conditions, rather than input conditions as previously. This essentially assume pK uncertainty is constant regardless of temperature and pressure. 4) [CO2(aq)] is calculated from fCO2 and K0 every time, rather than from different combinations of parameters. 5) Substrate-inhibitor ratio (Bach, 2015) is included as an output argument from CO2SYS, rather than calculated in a separate function. 6) Input uncertainty in [CO2], [HCO3], and [CO3] should now be input as an absolute value in mol/kg, rather than a relative value. 7) Modifications were made to allow vector inputs with different input parameters in each row to errors.m. 8) Derivatives and errors for Revelle factor are now calculated. Be advised: The addition of substrate-inhibitor ratio to the CO2SYS output arguments (positions 20 and 38) have changed the column references for all output arguments after position 20. Similarly, the addition of Revelle factor uncertainty to the errors.m output arguments (positions 9 and 19), along with the retention of uncertainty from input arguments, has changed the column references for output arguments from errors.m.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.370 | 0.319 |
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".