Bibliographic record
Abstract
Introduction Garrels and Mackenzie (1967) introduced inverse mass balance modeling into geochemistry. They showed that if the chemistry of the start and end solutions are known, possible mass transfer reactions that had produced the compositional differences and the extent to which these reactions had taken place could be deduced from the mass balance principle. Plummer and co-workers (Plummer, 1985; Plummer et al. , 1983, 1990, 1991, 1994; Wigley et al. , 1978) used this concept to model groundwater aquifers, and greatly expanded and formalized this approach. Their main interest was to deduce the mass transfer reactions taking place between two observation points along a flow path, which may have been responsible for the chemical and isotopic evolution of the groundwater. Mass transfer here refers to simple mass transfer between two or more phases, such as dissolution and precipitation of minerals (e.g., Nordstrom and Munoz, 1985). The development of the program netpath by Plummer and co-workers (Plummer et al. , 1991, 1994) has greatly facilitated the use of this modeling approach. Recent development of a new version of phreeqc by David Parkhurst (Parkhurst, 1995, 1997) incorporates uncertainty analysis and a more complete set of mass balance constraints, reaching a new level of model sophistication. We omit the mathematical development here. Serious modelers should read Parkhurst (1997), Plummer et al. (1991, 1994), and Wigley et al. (1978). Readers are also encouraged to read very carefully the work by Plummer et al. (1990) on the Madison aquifer, Montana, which demonstrates splendidly the application of inverse mass balance modeling in a regional aquifer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".