Carbonate chemistry of competitive dissolution experiment
Bibliographic record
Abstract
The archived dataset was collected during a laboratory experiment, conducted between April 2021 and April 2022 at McGill University, in which we tested the competitive carbonate dissolution hypothesis. A mixture of biogenic and synthetic carbonates was reacted with acidified, natural seawater to simulate the progressive acidification of ocean waters by anthropogenic carbon dioxide (CO2). The biogenic material (Goniolithon, a high-magnesium (Mg) calcite red algae, and Halimeda, an aragonitic green algae) was collected off the coast of Andros, Bahamas in November 2019, whereas the ACS-grade synthetic calcite was purchased from Fisher Scientific®. The natural seawater was collected at ~400 m depth in the Gulf of St. Lawrence. The reaction was conducted in 350 mL of natural seawater with 0.3133 g of Goniolithon, 0.3020 g of Halimeda and 0.3068 g of synthetic calcite in a 500 mL water-jacketed glass reaction vessel maintained at 25.0 °C by recirculating water from a constant temperature bath through the reactor jacket. Results of this study confirm the hypothesis that carbonates will dissolve sequentially according to their respective solubility. They also reveal that the dissolution of high Mg-calcites proceeds incongruently. The originality of this contribution rests with the demonstration that the presence of a single high Mg-calcite will generate, like in a sediment of mixed mineralogy, a continuum of transient states as lower Mg-calcites of greater stability are precipitated and dissolved.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".