MAP-IO (Marion Dusfresne Atmospheric Program - Indian Ocean) flow cytometry
Bibliographic record
Abstract
The MAP-IO (Marion Dusfresne Atmospheric Program - Indian Ocean) program aims to make up for the lack of observation in this region of the earth by equipping the Marion Dufresne vessel (https://taaf.fr/en/marion-dufresne-and-astrolabe/) with a set of in-situ instruments and remote sensing for the atmosphere and marine biology studies. This program has been labeled by the French Commission Nationale de la Flotte Hauturière (CNFH, https://www.flotteoceanographique.fr/) for the period 2021 to 2024. During this period, MAP-IO will operate as a scientific program for the acquisition and scientific enhancement of four years of data. This period will also serve as an operational prototype to study the feasibility of switching the program to a permanent observatory aimed at integration into international infrastructures networks such as ACTRIS (https://www.actris.eu/) or ICOS (https://www.icos-cp.eu/). More informations on the project : http://www.mapio.re/. MAP-IO is a scientific program led by the University of La Reunion (LACy and OSU-R) and was funded by the European Union through the ERDF programme, the University of Reunion, the SGAR-Réunion, the Région Réunion, the CNRS, IFREMER and the Flotte Océanographique Française. The Cytosense automated flow cytometer from the cytobuoy compagny was installed onboard the Marion Dufresnes Sea Water supply, to run semi continuously samples for phytoplankton functional groups resolution. Sample acquisition was schedulled once avery two hours. The data corresponds to abundances in cells/ml, mean forward scatter and red fluorescence in arbitrary units, per group. The groups are identified as standard groups following the BODC F02 vocabulary and the corresponding selections sets named following expert names.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.023 |
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".