Bibliographic record
Abstract
Terrestrial vegetation contributes strongly to dynamic biosphere-atmosphere exchanges of mass and energy, through activities such as photosynthesis, that help shape the Earth’s climate. The boreal forest is located in high latitudes and subject to large seasonal temperature fluctuations and a changing climate. Understanding the response of the boreal forest to seasonal and climate changes requires a means of effectively monitoring vegetation phenology at large spatial and temporal scales. Optical remote sensing can be applied at such scales, providing a powerful means of observing how ecosystems respond to changing environmental conditions. However, continued work that integrates both optical remote sensing and plant physiology at multiple scales is necessary to correctly apply and interpret large scale remote sampling of vegetation. Key questions regarding the application of optical remote sensing across ecosystems remain unanswered: 1) which remote sensing metrics are most effective at monitoring phenology of different functional types? and 2) how do these remote sensing metrics relate to actual changes in plant physiology when sampling different vegetation? To address these questions, experimental forest stands for several boreal tree species, both evergreen and deciduous were established in pots in Edmonton, Alberta, Canada, allowing for continuous monitoring across seasons using a variety of metrics to track phenology of representative boreal vegetation at multiple scales. This involved the use of different optical indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), the chlorophyll/carotenoid index (CCI), and steady-state chlorophyll fluorescence (FS), as an analogue of solar-induced fluorescence (SIF). These optical metrics were then compared to actual rates of photosynthesis to determine their efficacy in tracking seasonal changes in photosynthetic activity, or photosynthetic phenology. Results indicated that NDVI and PRI exhibited a complementary ability to monitor photosynthetic phenology of both evergreen and deciduous functional types. NDVI effectively tracked photosynthetic phenology of deciduous species, but less so for evergreens, while PRI closely paralleled photosynthetic phenology of evergreens, but less so for deciduous species. CCI showed strong parallels with photosynthetic activity in both evergreen and deciduous species, with FS showing a similar ability. These results indicated subtle differences in seasonal patterns of optical metrics and photosynthetic activity across and within functional types. Overall, these results revealed the efficacy of different remote sensing metrics at tracking photosynthetic phenology of different boreal tree species. This project provides an important foundation for the assessment of plant physiology by means of optical remote sensing, expanding the value of large-scale ecosystem monitoring.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".