Functional trait database for Nova Scotian coastal barren, green roof, and ruderal flora
Bibliographic record
Abstract
This is the first plant functional trait database for Nova Scotia, Canada. The data contained here were collected between 2016 and 2019 from locations around Halifax, Nova Scotia. The species selected for trait collection were chosen based on species inventories taken across Nova Scotian coastal barrens and from green roofs at Saint Mary's University. The purpose of the coastal barren trait data was to understand community assembly in this understudied ecosystem. The green roof inventory was included as coastal barren species are known to succeed on green roofs in Nova Scotia. The green roof trait data was used to answer questions surrounding coexistence and trait divergence, and community assembly and spatial heterogeneity. In total, this database contains 14,341 trait values from 203 species comprising 130 genera and 53 families. The majority of species are commonly found on coastal barrens (84 species), disturbed sites (48 species), and forests (27 species). Additionally, this database contains trait data for 30 species that have been successfully established (survival for >1 year) on green roofs in Nova Scotia and ruderal species that commonly colonize both green roofs and coastal barrens. This database contains 12 plant functional traits: leaf thickness (203 species), leaf area (203 species), specific leaf area (203 species), leaf dry matter content (203 species), plant height (203 species), canopy width (203 species), seed mass (79 species), seed shape (61 species), root radius (22 species), leaf phosphorus content (3 species), leaf nitrogen content (30 species), and leaf carbon content (30 species). The species in this database can be subdivided into 10 growth forms, with the majority of species characterized as forbs (75 species), shrubs (56 species), or graminoids (33 species). This data set is freely available for scientific use; when used in published analyses, this paper should be referred to as the data source.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".