Bibliographic record
Abstract
Abstract. Over the past 250 years, atmospheric CO2 concentrations have risen steadily from 277 ppm to 405 ppm, driving global climate change. In response, new technologies are being developed to reduce emissions and to remove carbon from the atmosphere using negative emission technologies (NETs). One proposed NET is Ocean Alkalinity Enhancement (OAE), which would mimic the ocean’s natural weathering processes, raising alkalinity and pH and sequestering carbon dioxide from the atmosphere. The potential impacts of OAE were assessed through an analysis of prior studies investigating the effects of elevated pH on phytoplankton growth rates and by experimental assessment of the pH-dependence of viability and growth rates in two near-shore isolates of phytoplankton. Viability was assessed with a modified Serial Dilution Culture – Most Probable Number assay. Chlorophyll a fluorescence was used to test for changes in photosynthetic competence and apparent growth rates. There were no significant impacts on the viability or growth rates of the diatom Thalassiosira pseudonana and the prymnesiophyte Diacronema lutheri (formerly Pavlova lutheri) with short-term (10-minute) exposure to elevated pH. However, there was a significant decrease in growth rates with long-term (days) exposure to elevated pH. Short-term exposure is anticipated to more closely mirror the natural systems in which OAE will be implemented because of system flushing and dilution. These preliminary findings suggest that there will be little to no impact on a variety of taxonomic groups of phytoplankton when OAE occurs in naturally flushed systems.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.231 | 0.143 |
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".