Nutrient Dynamics Buoy (Ndb) Sensor Data and Calibration Report
Bibliographic record
Abstract
The Nutrient Dynamics Buoy (NDB) is currently based on a multi-parameter sonde with 7 sensors and includes an integrated Nitrate Sensor and a Phosphate sensor. It also includes a reference PAR1 sensor mounted at the water surface as well as a submersible PAR1 sensor that is immersed at 2m below the water surface together with the mult-parameter sonde, Nitrate sensor and Phosphate sensor.The multi-parameter sonde monitors the temperature, depth, conductivity, pH, turbidity, chlorophyll and dissolved oxygen. It also has an anti-fouling wiper mechanism that removes any debris that may accumulate on the sensors during extended deployment.The immersed PAR1 sensor also has an anti-fouling wiper mechanism, while the reference PAR1 sensor at the surface has a cover that is opened just before the PAR1 measurements are made and then closed after the measurements have been taken.The readings for these sensors were recorded by a logger mounted in the NDB at the LV1 mooring at station 750 in Lake Ontario. This test report summarizes these results for the deployment during September of 2010.The calibration results for the sensors are included in the appendices.1PAR = Photosynthetically Available Radiation measured between 400nm and 700nm with a constant quanta response.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".