PhenoCam Images and Canopy Phenology at Harvard Forest since 2008
Bibliographic record
Abstract
A collaborative research network (“PhenoCam”) to provide automated "near" remote sensing of canopy phenology across the northeastern US and adjacent Canada has been initiated. A pilot study (2006-2007) at the Bartlett Experimental Forest previously demonstrated the viability of tracking both spring green-up and autumn senescence based on relative changes in red, green, and blue (RGB) color channel brightness values extracted from networked digital camera (“webcam”) images. In 2008, we installed commercial-grade digital webcams at a dozen established research sites across the northeast, from Ontario and New York across to Maine, with most sites concentrated within a few degrees of 45 deg N. Two additional Midwestern sites were subsequently added to the network. At seven camera sites (Bartlett, Howland, Harvard Forest, Groundhog River, Chibougamau, University of Michigan Biological Station, and Morgan Monroe State Forest), ongoing measurements of carbon and water fluxes are being made with the eddy covariance method, which will enable us to directly link phenology to seasonal variation in ecosystem processes. The seasonal trajectory of a “greenness index” for Harvard Forest shows a rapid rise in greenness in spring (coinciding with ground observations of budburst), a gentle decline over the course of the summer, and then a rapid decline with autumn senescence. This network is providing a very rich (and unique) dataset on spatial and temporal patterns of canopy phenology in the across this region. Half hourly images are uploaded to a project web page (http://phenocam.sr.unh.edu), which also features additional information about the project, including a protocol for camera deployment, download tools (so that the imagery is available to a wider community), site locations and contact information, etc. Image processing routines are under development. Time series of greenness will be added to the archive when this is complete; at present, only images are being made available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".