Prospective impacts of oil spills on floodplain vegetation: Both crude oil and diluted bitumen increase foliar temperatures, senescence and abscission in three cottonwood (Populus) species
Bibliographic record
Abstract
Oil pipelines are vulnerable at river crossings since floods can expose and rupture pipes, releasing oil that floats and coats floodplain vegetation. This study investigated the consequences of oil coatings on leaves of cottonwoods (riparian poplars), the predominant trees in floodplain woodlands around the Northern Hemisphere. The study compared conventional crude oil (CO) versus diluted bitumen (dilbit, DB), heavy oil originating from the Alberta oil sands; with petroleum jelly (PJ) as a reference. The treatments increased leaf surface temperatures (Tleaf) in narrowleaf and plains cottonwoods (Populus angustifolia, P. deltoides) and balsam poplars (P. balsamifera) (Control = 21.8°C, PJ = 23.7°C; CO = 26.2°C; DB = 28.1°C; Tair = 25°C). The leaf warming followed stomatal occlusion from the foliar coating, which would reduce transpiration and evaporative cooling, combined with increased solar warming with the darker oils. Tleaf varied across the three cottonwood species, with cooler, narrow, narrowleaf cottonwood leaves; intermediate plains cottonwood leaves; and warmer, darker, balsam poplar leaves (average Tleaf: narrowleaf = 23.8°C, plains = 24.3°C, and balsam = 26.7°C), with similar warming in each species following the different treatments. Across species and treatments, Tleaf was tightly correlated with foliar condition, which assessed turgor versus wilting of leaf blades and petioles, along with leaf necrosis and senescence (r2 = 0.980, narrowleaf; 0.998, plains; 0.852, balsam). This tight association indicates validity of both Tleaf and foliar condition as diagnostic measures. Crude oil and dilbit had similar foliar impacts, and for both, leaf abscission occurred within 2 to 3 weeks. Consequently, following an oil spill, remediation should commence quickly but extending vegetation removal beyond a few weeks would have limited benefit since the contaminated leaves would have abscised.
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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 | 0.000 | 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".