Zooplankton faecal pellet size characteristics and role in carbon fluxes
Bibliographic record
Abstract
These datasets were gathered from 197 scientific articles. They were analysed in the scientific paper Perhirin et al. (to be submitted). Data were splitted into two datasets : Size&Biomass.csv - Variable : variable concerned (length, width, volume, density or Ccontent) - IndividualOrAggregated : Individual : data measured at one time and one place (for variables related to faecal pellet role in POC flux) or measured on one faecal pellet (for variables related to morphology and biomass) Aggregated : averaged data (over time and/or space for variables related to faecal pellet role in POC fluxes, or over multiple faecal pellets for variables related to their morphology and biomass) - Value : only if IndividualOrAggregated = ‘Individual’ - Mean : only if IndividualOrAggregated = ‘Aggregated’ - SD : only if IndividualOrAggregated = ‘Aggregated’ and available - NewUnit : unit after process, should be used - FormerUnit : unit before process, should not be used - MonthNumber : month (or range of months) of sampling (between 01 and 12) - Year : year (or range of years) of sampling (between 1967 and 2022) - LatDegDec : latitude of sampling (in decimal degree, °N) - LonDegDec : longitude of sampling (in decimal degree, °E) - DepthN : depth of sampling (in m) - DepthMinN : minimal depth of sampling (for net sampling) - DepthMaxN : maximal depth of sampling (for net sampling) - DepthMean : mean depth of sampling (mean of minimum and maximum, for net sampling) - Producers/Pellet characteristics : information about the zooplankton that produced the concerned faecal pellet(s) or about the faecal pellet(s) - Type : type of the concerned faecal pellets, either derived from the producers or the pellet characteristics, defined in Perhirin et al. (to be submitted) - Ocean : sampled location - Method : method used to sample or to produce the concerned faecal pellet(s) - Reference : scientific paper from which data were extracted - DOI : DOI of the match scientific paper from which data were extracted - DataAcquisitionNotes : note if data were manually extracted from a figure or a table 2. Contribution&Flux.csv In addition to the 22 variables similar as the first dataset, the 4 following variables were included - Chl : surface chlorophyll-a concentration [Chl-a] derived from MODIS monthly climatology (L3 mapped product, 4 km, MODIS-Aqua Ocean Color Data, https://oceandata.sci.gsfc.nasa.gov/l3/) - Productivity : discrete [Chl-a] categories defined as follow low productive regions ([Chl-a] < 0.1 mg m-3), moderate productive regions (0.1 mg m-3 < [Chl-a] < 1 mg m-3) and high productive regions (1 mg m-3 < [Chl-a]). - sst : sea surface temperature (SST) derived from MODIS monthly climatology (L3 mapped product, 4 km, MODIS-Aqua Sea Surface Temperature Data, https://oceandata.sci.gsfc.nasa.gov/l3/) - TempCategory : discrete SST categories defined as follow cold waters (SST < 5°C), medium waters (SST between 5°C and 15°C) and warm waters (SST > 15°C).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".