Germination characteristics of tropical and sub-tropical rangeland species
Bibliographic record
Abstract
A study was made of the germination characteristics of a broad spectrum of rangeland species by studying their behaviour under different conditions. Seeds of common species (both native and exotic) were collected from tropical (north-east Queensland) (36 species) and sub-tropical areas (south-east Queensland) (47 species). The seeds were exposed to three storage treatments: in a shade-house for 60 months, in a seed store (tropical collection) or freezer (sub-tropical collection) for 60 months, or in an oven with fluctuating temperatures (25/60°C) for 3 (tropical collection) or 4 (sub-tropical collection) months. Germination was tested during and after storage under standard conditions of 30/25°C (tropical collection) or 30/20°C (sub-tropical collection) with light during the 12-h period of higher temperature. In addition, germination of the sub-tropical collection was tested in the dark and at lower temperature (20/10°C). The species were divided into groups on the basis of changes in germination during storage in a shade-house or in a seed store or freezer. The species showed a wide range of germination behaviour, changes during storage, and responses to germination conditions. Differences in the responses of seed lots of the same species in the two collections show that care is needed when extrapolating results from one experiment to other collections and regions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".