SMAPVEX19-22 Millbrook Canopy Photos
Bibliographic record
Abstract
This project was initiated in 2019 and completed in 2022. For this campaign, sites were based in the Millbrook NY area, approximately centered at Cary Institute of Ecosystem Studies and in Massachusetts approximately centered at Harvard Forest. A third site for part of the campaign was centered at a Boreal Ecosystem Research and Monitoring Sites (BERMS) in Saskatchewan. Each site had approximately 25 sub-sites located within a 30x30 km grid that coincided with the path of the SMAP satellite. At each sub-site, stations were installed to collect soil moisture, soil temperature, air temperature and canopy photo data. Each station had 3 soil moisture and temperature sensors placed vertically, at 5 cm below surface and 5 cm below mineral soil. Also, each station had an air temperature sensor and an upward facing camera for phenology. In addition to these stations, at six sites, Teros water content sensors were installed in trees and at 4 sites sapflow sensors were placed in trees. During April and August 2022, vegetation and intensive manual measurements were made at each of the 25 sites. In addition, several remote sensing datasets were collected.Data in these datasets include photos from upward facing wildlife cameras with timelapse photos taken during daylight hours at each station. Photos are available on request, or you can access them via Google Drive here: Cary institute Canopy Photos
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.131 |
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".