Alpine tundra plant litter decomposition rates and nutrient content, Kluane Lake, Yukon Territory, Canada (2015-2018)
Bibliographic record
Abstract
In this 4-year study we experimentally manipulated shrub presence (shrub present or removed) and litter quantity (none, natural abundance, and 2 times the natural abundance) in a fully factorial design. Within each experimental plot we measured litter decomposition rates of the dominant shrub (Betula nana) over three years - litter was collected and litter bags created in the fall of 2015, and bags collected in the fall of 2016, 2017 and 2018. We also conducted a single year decomposition experiment within each plot using three species - a tall deciduous shrub (Betula nana), a dwarf deciduous shrub (Salix reticulata) and a graminoid (Carex consimilis). For this experiment, litter was collected and litter bags created in fall 2016 and litter bags collected in fall 2018. %Carbon and %Nitrogen of litter was determined at initial collection and each litter bag collection time point for both experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".