Measurements of canopy interception and transpiration of openly-grown eastern redcedar in central Okalhoma
Bibliographic record
Abstract
Eastern redcedar (Juniperus virginiana L.) is rapidly encroaching and degrading native prairie and rangeland landscapes in the Great Plains of the U.S. Little is known concerning the impacts of increasing redcedar density and areal coverage on local and regional water budgets through transpiration (Tr) and canopy interception (CI) of precipitation. Limited Tr and CI studies have been conducted in dense stands of redcedar but results from these studies may not be applicable to redcedar growing in open environments. Four redcedar trees (two large, two small) were located in central Oklahoma to measure Tr. Two limbs (one on the north face and one on the south face) on each of the large trees were instrumented with sapflux sensors to measure Tr from August 2010 through mid-July 2012. Limb level Tr was scaled to tree level Tr using ratios of both leaf and bole areas. Whole tree Tr was measured on two small redcedar trees from mid-May 2011 through mid-July 2012. Transpiration of the small redcedars was found to respond quickly to precipitation events, while the large redcedars did not. Redcedar Tr was compared to that of native grasses. The large redcedars exhibited higher Tr rates than native grasses while the small redcedars transpired at rates closely matching native grasses. Four different redcedars were instrumented to measure CI from October 2009 through mid-July of 2012. Redcedar canopies were found to intercept 100% of precipitation for events ? 2.4 mm. Redcedar canopies reduce annual precipitation received at the surface by about 33%, and as much as 39% in the western portion of the state. Significant canopy interception of precipitation, coupled with Tr rates as large as or larger than native grasses and with year-round Tr, suggests increases in redcedar density and areal coverage could affect local water resources (e.g. reducing infiltration, runoff, and ground water recharge rates).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".