Referee report. For: Cladonia lichens on extensive green roofs: evapotranspiration, substrate temperature, and albedo [v1; indexed, http://f1000r.es/2ha]
Bibliographic record
Abstract
Green roofs are constructed ecosystems that provide ecosystem services in urban environments. Shallow substrate green roofs subject the vegetation layer to desiccation and other environmental extremes, so researchers have evaluated a variety of stress-tolerant vegetation types for green roof applications. Lichens can be found in most terrestrial habitats. They are able to survive extremely harsh conditions, including frequent cycles of desiccation and rehydration, nutrient-poor soil, fluctuating temperatures, and high UV intensities. Extensive green roofs (substrate depth <20cm) exhibit these harsh conditions, making lichens possible candidates for incorporation into the vegetation layer on extensive green roofs. In a modular green roof system, we tested the effect of Cladonia lichens on substrate temperature, water loss, and albedo compared to a substrate-only control. Overall, the Cladonia modules had significantly cooler substrate temperatures during the summer and significantly warmer temperatures during the fall. Additionally, the Cladonia modules lost significantly less water than the substrate-only control. This implies that they may be able to benefit neighboring vascular plant species by reducing water loss and maintaining favorable substrate temperatures.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.566 | 0.192 |
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".