Microphytobenthos Primary Production Estimated by Hyperspectral Reflectance
Bibliographic record
Abstract
Monitoring photosynthesis is a great challenge to improve our knowledge of plant productivity at the ecosystem level, which may be achieved using remote-sensing techniques with synoptic abilities. The main objective of the current study is to take up this challenge for microphytobenthos (MPB) primary production in intertidal mudflats. This was achieved by coupling hyperspectral radiometry (reflectance, ρ and second derivative, δδ) and PAM-fluorometry (non-sequential light curve, NSLC) measurements. The later allowed the estimation of the primary production via the light use efficiency (LUE) and the electron transport rate (ETR) whereas ρ allowed to estimate pigment composition and optical absorption cross-section (a*). Five MPB species representative of the main growth forms: epipelic (benthic motile), epipsammic (benthic motile and non motile) and thycoplanktonic (temporarily resuspended in the water column) were lighted at increasing light intensity from dark to 1950 µmol photons.m-2.s-1. After spectral measurements, a* was retrieved using a radiative transfer model and several radiometric indices were tested for their capacity to predict LUE and ETR. The spectral estimation of these two photosynthetic variables was subsequently compared to the values estimated by PAM-fluorometry. Results showed that different responses related to the xanthophyll cycle (de-epoxydation state) were observed for the three growth-forms with increasing light levels. However, a single relationship with radiometric index was not affected by species/growth-forms, i.e. δδ496/508, called the MPBLUE index to predict LUE and ETR. This index has the potential to be applied to air borne hyperspectral imagery for large-scale assessment of MPB production.
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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) | 0.000 | 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 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".