Seed size in lacustrine and riverine populations of wild rice in northern Minnesota and Wisconsin
Bibliographic record
Abstract
Anecdotal information gathered from contemporary wild rice harvesters, traditional ecological knowledge of indigenous peoples, and biologists suggests that seeds produced by wild rice ( Zizania palustris L.) in riverine habitats are smaller than those produced in lacustrine habitats. To study the differences in the seed size of wild rice between lakes and rivers, four river and four lake pairs were sampled to measure and model the factors affecting seed size. We found mean seed mass to be quantitatively different between lacustrine and riverine environments; seed mass in lake populations was (41%) larger than that in river populations. When partitioned between water body type, regional population pairs, and individual populations, water body type accounted for 71.3% of the variance. Data collected on seed mass, plant morphology, sediment characteristics, and water depths were used to create a statistical model to quantify the effects of each factor on seed size. The two most important environmental factors contributing to seed size were sediment bulk density and water depth at seed collection. Important biological components were seed scar density, proportion of filled seed, and root biomass.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".