Understanding vegetation response to climate variability from space: the scientific objectives, the approach and the concept of the SPECTRA mission
Bibliographic record
Abstract
The goal of the SPECTRA mission is to improve the description of those processes by means of better constraints on and parameterizations of the associated models. The prime objective of SPECTRA is to determine the amount, assess the conditions and understand the response of terrestrial vegetation to climate variability and its role in the coupled cycles of energy, water and carbon. The amount and state of vegetation will be determined by the combination of observed vegetation properties and data assimilation. Specifically, the mission will characterize the amount and state of vegetation with observations of the following variables: (1) fractional vegetation cover; (2) Fraction Absorbed Photosynthetically Active Radiation (FAPAR); (3) albedo; (4) Leaf Area Index (LAI); (5) leaf chlorophyll content; (6) leaf water content; (7) foliage temperature; (8) soil temperature; (9) fractional cover of living and dead biomass. SPECTRA will provide spatially distributed observations (maps) of the key vegetation properties at the spatial resolution of one image pixel and a temporal frequency of one week or lower. Each map will cover an area of 50 km/spl times/50 km. The SPECTRA mission is being studied by the European Space Agency to address these scientific issues. The mission comprises the following elements: a space segment consisting of an imaging spectrometer covering the region 400 nm-2400 nm with a nominal spectral resolution of 10 nm and of an agile platform to perform subsequent, along track observations at seven view angles between -70/spl deg/ and +70/spl deg/; a ground segment consisting of a core data processing facility and specialized Centers of Excellence to guarantee to a wide and diverse community access to higher level data products and to specialized data assimilation systems; and a field segment consisting of 50 to 100 dedicated sites where teams of investigators evaluate the observations and assimilate them in models describing the functioning of terrestrial ecosystems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".