Data for: Modeled seed accumulation patterns explain spatial heterogeneity of shrub recruitment within the taiga-tundra ecotone
Bibliographic record
Abstract
These data were collected to investigate the fine-scale drivers of tundra shrub recruitment patterns with a specific focus on seed dispersal and ground cover suitability. To do this we established 15 m resolution grids of seed traps and paired seedling abundance measures at three Alnus alnobetula (green alder) patches near the Trail Valley Creek Research Station situated within the taiga-tundra ecotone of the Northwest Territories. For each observation (i.e., target point) we estimated the relative seed input expected based off a set of seed dispersal mechanisms hypothesized to be important at the site. These included overland hydrochory (OVERLAND), blowing snow transport (SNOW), dominant wind direction (WIND), and distance from source (DISTANCE). We also estimated topographic wetness index (TWI) for each target point. The model-averaged relationship between these mechanisms and seedling abundance was then used to predict seedling recruitment across the Siksik creek sub-catchment by estimating the seed input from each hypothesized mechanism across 2000 random points. To validate these predictions and the resulting map, we selected 51 points across the basin and estimated seedling abundance by measuring the distance to the third nearest seedling from each point. In addition to the grid and validation data, this dataset also includes information on the abundance of alder seedlings across topographic gradients at ten sites. Each site included transects running through alder patches, with sampling quadrats established above the patch, at the exterior and interior of both patch edges and at the patch centre. The patch transects were paired with transects in the adjacent alder-free tundra which included top, middle, and bottom sampling quadrats. At each of these quadrats we established two 1 m2 vegetation plots in which we estimated ground cover abundance and counted the number of alder seedlings, allowing for the investigation of plot-level associations. Five of these sites were then selected for intensive ground cover surveys to investigate the proportions of seedlings growing directly out of specific ground cover types. For detailed methodological information see Wallace et al. 2024. Modeled seed accumulation patterns explain spatial heterogeneity of shrub recruitment within the taiga-tundra ecotone. In review at JGR:Biogeosciences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".