Supplement 1. Data used in the four applications of principal coordinates of neighbor matrices (PCNM) analysis: abundance of ferns, transect coordinates, and environmental variables (Peru); biomass of zooplankton, transect coordinates, and environmental variables (Guadeloupe); chlorophyll a and spatial coordinates (Thau, France); and oribatid mite species counts, spatial coordinates, and environmental variables (St-Hippolyte, Québec).
Bibliographic record
Abstract
File List Amazonian fern example: Nauta.txt Response, spatial, and environmental variables for the Nauta transect Huanta.txt Response, spatial, and environmental variables for the Huanta transect Guadeloupe zooplankton example: Guadeloupe.txt Response, spatial, and environmental variables Chlorophyll a example: Thau_data.txt Response and spatial variables Oribatid mites example: Oribatida_species.txt Response variables Oribatida_XY_envir.txt Spatial and environmental variables Description Data used in the four examples. Quantitative values for the dependent and spatial variables. Environmental variables may be quantitative or binary. Amazonian fern example: Description of the variables for both transects: Response variable: raw counts of the fern Adiantum tomentosum in 5 × 5 m quadrats. Spatial variable: coordinate (m) of the center of the plot along the transect. Environmental variables: elevation (m), number of stems of trees of five classes of diameters at breast height (cm), number of stems of lianas of three classes of stem diameter (cm), thickness of the layer of soil organic matter, drainage in five classes (0 = good, 5 = none), canopy height (m), canopy, shrub and herbaceous coverage (cm) Guadeloupe zooplankton example: Response variable: log-transformed zooplankton biomasses of two size classes (original units: mg/m3 ash-free dry mass). Spatial variable: coordinate (km) of the sampling site along the transect. Environmental variables: dissolved oxygen (mg/L), salinity (psu), wind speed (m/s), phytoplankton biomass (log-transformed, original units: µg/L), turbidity (NTU), swell height (m) and 14 binary variables coding for habitat classes. Chlorophyll a example: Response variable: chlorophyll a biomass (µg/L). Spatial variable: X and Y coordinates (km) of the site. Oribatid mites example: Response variable: raw counts of 35 species of oribatid mites on a Sphagnum mat . Spatial variable: X and Y coordinates (m) of the sampling cores. Environmental variables: bulk density of the substratum (g/L of dry uncompressed matter), water content (g/L of raw uncompresed material) and 12 binary variables coding for classes of substratum: 4 species of Sphagnum moss, ligneous litter, bare peat, interface between two substrates, 3 categories of shrubs, microtopography (blanket or hummock).
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.811 | 0.267 |
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".