Monitoring the Spring Bloom in an Ice Covered Fjord with the Land/Ocean Biogeochemical Observatory (LOBO)
Bibliographic record
Abstract
The Land/Ocean Biogeochemical Observatory (LOBO) system was initially developed by MBARI under the NSF Biocomplexity program to monitor the land/ocean interface for coastal zone management. The system utilizes highly robust and accurate sensors to provide sustained monitoring of critical watershed habitats in challenging environmental conditions in real time. With the incorporation of novel anti-fouling technology developed by WET Labs, the system has an unprecedented six week maintenance cycle, greatly reducing operational costs while providing high quality data sets. LOBO measurements include nitrate, dissolved oxygen, conductivity, temperature, chlorophyll fluorescence, turbidity and CDOM. A LOBO was deployed in the Northwest Arm of Halifax Harbour in January of 2007 and has been monitoring the biogeochemical parameters continuously since (see http://lobo.satlantic.com ). Despite heavy icing at -20degC, heavy winds, and sea ice the system reported data every hour where the detailed nature of the annual spring phytoplankton bloom was recorded at high resolution. The coincident and continuous record of nutrients, phytoplankton and other physical and chemical parameters is unique, and provides a robust means to base predictive coastal ecosystem models in sensitive marine areas.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".