Data from "Every little helps: the functional role of individuals in assembling any plant community, from the richest to monospecific ones"
Bibliographic record
Abstract
Spectral and functional data used for massive phenotyping using Vis-NIR spectrometry. Visible and Near Infrared absorbance spectra were collected in July 2020 using the LabSpec 4 Standard-Res Lab Analyzer (Malvern Panalytical) from 1-4 sets of ten Pinus sylvestris needles for 170 trees in a forest stand located at the tree-line of the Guadarrama National Park (1900 m asl; Lat. 40.81, Long. -3.95). Average absorbance spectrum of each needle set is included as a row in the Spectral Data sheet. For this sample set, we used a constant spectral resolution value of 1 nm, which produced 2151 spectral points between 350 and 2500 nm. Needle sets were functionally characterized through three functional traits (LT – Leaf Thickness, SLA – Specific Leaf Area and LDMC – Leaf Dry Matter Content) which were measured according to standardized protocols (Cornelissen et al., 2003). Specifically, for each set, we weighed ten fresh well-developed needles using a microbalance (Mettler Toledo MX5, Columbus, OH; weight uncertainty ±1 μg). Projected surface area of ten needles was estimated with a digital scanner (Epson Perfection 4870) and WinFolia software (Régent Instruments, QC, Canada). The needles were then oven-dried at 60°C for 72 hours, and weighed to obtain dry mass. We also estimated leaf thickness using a dial thickness gauge (Mitutoyo Co., Aurora, IL, USA).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".