Processing and Data for "A Census of Quality-Controlled Biogeochemical-Argo Float Measurements"
Bibliographic record
Abstract
*** Note: The first version had an incorrect bgc.float.qa.py uploaded. Some aesthetic changes were made during the proofing process, so the current version here aligns with the publication. Additionally, the tables originally formatted in an older version of Zenodo no longer appear to work properly, so I will have to come back and reformat them. Summary: This document contains the associated data and software for the article "A census of quality-controlled biogeochemical-Argo float measurements". Data Citation: Stoer, A.C., Takeshita, Y., Maurer, T., Begouen Demeaux, C., Bittig, H., Boss, E., Claustre, H., Gordon, C., Greenan, B., Johnson, K., Johnson, K., Organelli, E., Sauzède, Raphaëlle, Schmechtig, C., and Fennel, K. (2023). Processing and Data for "A census of quality-controlled biogeochemical-Argo float measurements". Zenodo. doi: 10.5281/zenodo.8322118. Journal Article Citation: Stoer, A.C., Takeshita, Y., Maurer, T., Begouen Demeaux, C., Bittig, H., Boss, E., Claustre, H., Gordon, C., Greenan, B., Johnson, K., Johnson, K., Organelli, E., Sauzède, Raphaëlle, Schmechtig, C., and Fennel, K. (2023). A census of quality-controlled biogeochemical-Argo float measurements. Front. Mar. Sci. File Description: bgc.float.qa.py: This software extracts the quality data from the BGC-Argo database. The software provides a summary of data quality and quantity for each BGC variable of interest. You will need to download the data locally prior to running this software. More details can be found in the code itself. ** The tables below are no longer functional with the Zenodo update ** Data Variables Available in fig_1a_data.csv. Variable Name Description Unit year Year float was deployed Year oxy_floats Number of floats with an oxygen sensor deployed Floats Deployed nit_floats Number of floats with a nitrate sensor deployed Floats Deployed ph_floats Number of floats with a pH sensor deployed Floats Deployed chla_floats Number of floats with a chlorophyll-a fluorescence sensor deployed Floats Deployed bbp700_floats Number of floats with a particle backscattering sensor deployed Floats Deployed pared_floats Number of floats with PAR + irradiance sensor deployed Floats Deployed atleast_1_floats Number of floats with at least 1 BGC sensor deployed Floats Deployed all_6_floats Number of floats with all 6 BGC sensors deployed Floats Deployed Data Variables Available in fig_1b_data.csv. Variable Name Description Unit year Year float was deployed Year oxy_floats Number of active floats with an oxygen sensor Active Floats nit_floats Number of active floats with a nitrate sensor Active Floats ph_floats Number of active floats with a pH sensor Active Floats chla_floats Number of active floats with a chlorophyll-a fluorescence sensor Active Floats bbp700_floats Number of active floats with a particle backscattering sensor Active Floats pared_floats Number of active floats with PAR + irradiance sensor Active Floats atleast_1_floats Number of active floats with at least 1 BGC sensor Active Floats all_6_floats Number of active floats with all 6 BGC sensors Active Floats Data Variables Available in fig_2_data.csv. Variable Name Description Unit parameter BGC parameter of interest R n/ QC Real-time unadjusted and no QC flags Profiles R w/ QC Real-time unadjusted with QC flags Profiles A Real-time adjusted Profiles D Delayed-mode Profiles N No mode specified Profiles Data Variables Available in fig_3_data.csv. <var> indicates the BGC parameter of interest. Variable Name Description Unit year Year profiles were collected Year total_<var>_prof Total number of profiles Profiles hq_<var>_prof Number of high-quality profiles Profiles lq_<var>_prof Number of low-quality profiles Profiles ur_<var>_prof Number of profiles where the sensor is deemed unresponsive Profiles n_<var>_prof Number of profiles with missing QC flags Profiles Data Variables Available in fig_4_data.csv. Pre-2017 rates and float numbers are not pre-calculated. here. Variable Name Description Unit year Year profiles were collected Year total_<var>_prof Total number of profiles Profiles hq_<var>_prof Number of high-quality profiles Profiles lq_<var>_prof Number of low-quality profiles Profiles ur_<var>_prof Number of profiles where the sensor is deemed unresponsive Profiles n_<var>_prof Number of profiles with missing QC flags Profiles <var>_phq Number of profiles Profiles Data Variables Available in fig_5_data.csv. Pre-2017 rates and float numbers are pre-calculated here. Variable Name Description Unit year Year profiles were collected Year <var>_floats_deployed Total number of floats deployed Floats Deployed <var>_rfloat Float survival rate % Remaining at 36.5 Cycles <var>_rhq Float survival rate of high-quality profiles % Remaining at 36.5 Cycles Data Variables Available in fig_6_data.csv. Variable Name Description Unit parameter BGC parameter of interest geometry Polygon of each grid cell (need GeoPandas to plot) dens Density of high-quality profiles per area of ocean High-quality profiles km2 grid_area Area of ocean in the grid cell km2 Data Variables Available in fig_7_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest perc_g Density of high-quality profiles as a percentage of target density % Data Variables Available in fig_8_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest g_area_targ Total area of ocean where the profile density is above the target density km2 g_area_tot Total area of the region km2 p_area_targ Percentage of area where the the profile density is above the target density % Data Variables Available in fig_8_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest g_area_targ Total area of ocean where the profile density is above the target density km2 g_area_tot Total area of the region km2 p_area_targ Percentage of area where the the profile density is above the target density % Data Variables Available in fig_s1_data.csv. Variable Name Description Unit parameter BGC parameter of interest R n/ QC Real-time unadjusted and no QC flags Profiles R w/ QC Real-time unadjusted with QC flags Profiles A Real-time adjusted Profiles D Delayed-mode Profiles N No mode specified Profiles Data Variables Available in fig_s2_data.csv. Pre-2017 rates and float numbers are pre-calculated here. Variable Name Description Unit year Year profiles were collected Year <var>_floats_deployed Total number of float deployed Float Deployed <var>_rfloat Float survival rate % Remaining at 36.5 Cycles <var>_rfunc Float survival rate of functional profiles % Remaining at 36.5 Cycles Data Variables Available in fig_s3_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest dens Density of high-quality profiles in the region of interest High-quality profiles km-2 Data Variables Available in fig_s4_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest perc_g Density of high-quality profiles as a percentage of target density % Data Variables Available in fig_s5_data.csv. Variable Name Description Unit year Year profiles were collected Year region Marine region according to Flanders Marine Institute (2021) parameter BGC parameter of interest g_area_0 Total area of ocean where the profile density is above 0 km2 g_area_tot Total area of the region km2 p_area_min Percentage of area where the the profile density is above 0 profiles km-2 %
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".