Dataset: Inverse responses of species richness and niche specialization to human development
Bibliographic record
Abstract
Humans impact biodiversity by altering land use and introducing nonnative species. Yet the extent to which coexistence processes, such as competition and niche shifts, mediate these relationships is not clear. This dataset was used in a study that aims to compare how human development influences wetland plant diversity by examining patterns of species richness, niche specialization, and nonnative species occurrences along a human development gradient. This dataset can be used to analyzed species richness and niche specialization (a measure of the range of human development extents over which a species occurs) patterns from species occurrence data across 1582 wetlands in Alberta, Canada. Associations between human development extent and species richness, niche specialization, and nonnative species can be tested using linear mixed models. Also, nonmetric multidimensional scaling ordination can be applied from raw data (see usage notes) to examine whether community composition differed among wetlands surrounded by different human development extents. Note that human development data are accessible only through a data sharing agreement with ABMI. See the readme document for more details on how to obtain assess to these data. Results of these analyses can be found in the corresponding publication: Inverse responses of species richness and niche specialization to human development, Journal of Biogeography. https://doi.org/10.1111/jbi.14240
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".