In-situ particle size distribution, volume concentration, and other measurements collected from profiled LISST-200X sensor throughout the Bedford Basin during the Halifax Joint Learning Opportunity from 2024-09-25 to 2025-03-11 (NCEI Accession 0304370)
Bibliographic record
Abstract
This dataset contains time-series profiled measurements from a LISST-200X sensor (Sequoia Scientific, Inc.) collected during an ocean alkalinity enhancement (OAE) field trial conducted by Planetary Technologies in collaboration with Dalhousie University in Halifax, Nova Scotia, Canada. This dataset was collected under a Joint Learning Opportunity (JLO) funded by Carbon to Sea and COVE. The sensor was deployed to provide direct, in-situ measurements of particles and particle properties (e.g., size, concentration) to (1) address open questions related to alkaline feedstock transport and fate, as well as environmental impacts from alkalinity dosing (e.g., secondary precipitation), and (2) support the refinement and validation of models used to predict alkaline feedstock transport and dissolution kinetics. The LISST-200X was profiled at different locations and times throughout the Bedford Basin and surrounding waters from September 2024 to March 2025, both in and out of the mixing zone where alkalinity was being dosed. Profiling was performed during the approximately biweekly boat surveys conducted throughout the trial. The sensor measured particle size distribution, mean particle diameter, volume concentration, beam attenuation, and other variables. Additional information about the sensor deployment, profile locations, measurement parameters, data format, and processing can be found in the included supplemental document.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".