Database: Tidal Marsh Soil Organic Carbon (MarSOC) Dataset
Bibliographic record
Abstract
The repository is formatted in the following structure: - README.md: markdown file with repository description - MarSOC-Dataset.Rproj: R project file - useful when using RStudio - Maxwell_MarSOC_dataset.csv: .csv file containing the final dataset. The data structure is described in the metadata file. It contains 17,454 records distributed amongst 29 countries. - Maxwell_MarSOC_dataset_metadata.csv: .csv file containing the main data file metadata (equivalent to Table 1). - data_paper/: folder containing the list of studies included in the dataset, as well as figures for this data paper (generated from the following R script: ‘reports/04_data_process/scripts/04_data-paper_data_clean.R’). - reports/01_litsearchr/: folder containing .bib files with references from the original naive search, a .Rmd document describing the litsearchr analysis using nodes to go from the naive search to the final search string, and the .bib files from this final search, which were then imported into sysrev for abstract screening. - reports/02_sysrev/: folder with .csv files exported from sysrev after abstract screening. These files contain the included studies with their various labels. - reports/03_data_format/: folder containing all original data, associated scripts, and exported data. - reports/04_data_process/: folder containing data processing scripts to bind and clean the exported data, as well as a script testing the different models for predicting soil organic carbon from organic matter and finalising the equation using all available data. A script testing and removing outliers is also included.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.058 |
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".