Data from: Benthos response to nutrient enrichment and functional consequences in coastal ecosystems
Bibliographic record
Abstract
General description This dataset include the data collected during two experiments carried out in May and August 2019 to investigate the mechanisms involved in the benthic response to nutrient enrichment in coastal ecosystems. Sample were collected in an intertidal mudflat in Isle-Verte Bay (48° 2’ 36.76” N; 69° 21’ 3.736” W, St. Lawrence estuary, QC, Canada) in vegetated areas (Veg) and ice made-tidal pools (UnV). They were then exposed, in a well-controlled laboratory experiment, to three intensities of nutrient enrichment (N0, N1 and N2) during 30 days. Description of the dataset Data are organized in five tabular data files (.txt files; separator = tab, decimal = ., missing data = NA). Please refer to related publication for experimental design and analytical methods Sediment_characteristics.txt This file includes median sediment grain size (D50; µm), sediment porosity (volume ratio), Sediment organic matter (SOM; %Loss on ignition), Chlorophyll a content (Chl a; µg g-1), phaeopigments (µg g-1), Seagrass leaf biomasse (g) and Seagrass leaf elongation (cm day-1) Macrofauna_density.txt This file includes densities (individuals m-2) of sampled macrofaunal species Bioturbation.txt This files includes bioturbation metrics such as maximum penetration depth of luminophores (MPD; cm), Biodiffusion coefficient (Db; cm2 y-1) and porewater exchange rate (Q; mL m-2 h-1) Oxygen_dynamics.txt This file includes diffusive oxygen uptake (J; mmol m-2 d-1) and oxygen penetration depth (Z; µm) Nutrient_profiles.txt This file includes porewater nutrient (NH4+, NO3- and PO43-; µM) profiles within the sediment column (depth; cm) Benthic_fluxes.txt This file includes oxygen (TOU) and nutrients ((NH4+, NO3-) benthic fluxes (mmol m-2 h-1)
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.134 | 0.091 |
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".