Data from: Sea ice increases benthic community heterogeneity in a seagrass landscape
Bibliographic record
Abstract
These data include macrofaunal (i.e. > 0.5 mm) densities and biomasses and environmental data from the monitoring of a seagrass meadow located at the Manicouagan peninsula, Quebec, Canada. Sampling occurred during the year 2018 (April, June, August and October). Sample were collected in vegetated areas (V) and in artificial (aTP) and natural (nTP) tidal pools. Data are organized in five tabular data files (.txt files; separator = tab, decimal = .) (1) Manicouagan_biomasses.txt includes biomasses (g blotted wet weight/m2) of sampled macrofaunal species (2) Manicouagan_densities.txt includes densities (individuals/m2) of sampled macrofaunal species (3) Manicouagan_porewater_profiles.txt include depth profile of nutrient (NH4+, NO2 + NO3- and PO43-) concentrations (µM) (4) Manicouagan_Sediment_characteristics.txt includes median sediment grain size (D50; µm) sediment porosity (volume ration), particulate organic carbon content (OC; % Dry Weight), particulate total nitrogen content (TN; % DW), sediment chlorophyll a content (chla; µg/g), sediment phaeopigment content (µg/g), Total (Seagrass), above ground (AboGround) and below ground (BelGround) seagrass biomass (g/m2) and nutrients (NH4+, NO2 + NO3- and PO43-) stock (µmol/m2) in the first 8 cm of the sediment column. (5) Manicouagan_Bacteria.txt include extractible bacteria count (Abundance in number of cells per mL of sediment) and fluorescence (green fluorescence, relative unit) in the sediment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".